From csnet_gateway Sun Oct 12 18:34:35 1986 Date: Sun, 12 Oct 86 18:34:29 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe Subject: AIList Digest V4 #212 Status: R AIList Digest Friday, 10 Oct 1986 Volume 4 : Issue 212 Today's Topics: Query - Integer Equations, Expert Systems - Mathematical Models, Philosophy - Man's Uniqueness & Scientific Method & Understanding Horses & Irrelevance of Searle's Logic ---------------------------------------------------------------------- Date: Tue 7 Oct 86 10:36:01-PDT From: Ken Laws Subject: Integer Equations RIT Researchers Find Way to Reduce Transmission Errors, Communications of the ACM, Vol. 29, No. 7, July 1986, p. 702: Donald Kreher and Stanislaw Radziszowski at Rochester Institute of Technology have discovered a new geometry, the third 6-design, non-Euclidean geometry, that allows solution of difficult problems in designing error-correcting transmission codes. One problem with 99 integer equations and 132 unknowns was solved in 12 hours; previous search methods would have required several million centuries. Integer (Diophantine) equations are notoriously difficult to solve. Is this a breakthrough for other problem domains where search is used (e.g., bin packing, traveling salesman, map coloring, and the "approximately-solved" algorithms)? Is it a form of linear programming? -- Ken Laws ------------------------------ Date: 6 Oct 1986 13:08:20 EDT From: David Smith Subject: Expert systems and deep knowledge Grethe Tangen asked about using mathematical models of gas turbines as deep knowledge sources for diagnostics. GE in Schenectady, NY are working in this area. Bruce Pomeroy is perhaps the best contact, and he can be reached by mail to SWEET@a.isi.edu, or by phone at (518)387-6781. Hope this helps. DMS ------------------------------ Date: 7 Oct 86 15:52:00 GMT From: mcvax!unido!ztivax!bandekar@seismo.css.gov Subject: Mathematical Models I see some difficulties in using mathematical models of technical systems as a source of deep knowledge. Mathematical models are usually derived from the structural information about the devices, and one particular mo- del can represent more that one physical device. But I guess the approach would not be impossible as long as you can derive your device structure from your mathematical model. For example transfer function of several devices may be mathematically expressed in the same way. For multiple input/output plants the choice of state variables varies for state space representation. Which variables are affected if a particular physical component is defective and the causal ordering of the variables could be a valuable piece of know- ledge for the purpose of diagnosis. Here, if you can map your model into structural equations you may compute the causal ordering of the state variables.[Iwasaki,Simon '86]. Hierarchical representation of the technical systems is always useful. The concept of views[Struss, 86 to be presented at Sydney Univ. during Feb. 1987] is also important. If you can tell me more about your problem, I may be able to help out. my address: ... unido!ztivax!bandekar Vijay Bandekar :w :q ------------------------------ Date: Mon, 6 Oct 86 10:41:45 EDT From: "Col. G. L. Sicherman" Subject: man's godlike I'm amazed that nobody has responded to Peter Pirron's last argument: > The belief, that man's cognitive or intelligent abilities will > never be reached by a machine, is founded in the conscious or > unconscious assumption of man's godlike or godmade uniqueness, > which is supported by the religious tradition of our culture. It > needs a lot of self-reflection, courage and consciousness about > one's own existential fears to overcome the need of being unique. > I would claim, that the conviction mentioned above however > philosophical or sophisticated it may be justified, is only the > "RATIONALIZATION" (in the psychoanalytic meaning of the word) of > understandable but irrational and normally unconscious existential > fears and need of human being. Even net.ai, which is still a chaos of wild theories, has gone beyond regarding the a.i. question as a matter of science versus religion. Some arguments against Pirron's conjecture: -- If the objection to a.i. is rooted in cultural dogma, it's illogical to look at the psychology of the individual. Every individual is, now and always, unique--though some of us may feel that we are too much like others. This is quite another question than whether our species is unique. -- Other animals, and even plants, have intelligence--not to mention viruses! Many of us regard even a dog's intelligence as beyond the capabilities of a.i., at least in the way that scientists presently think about a.i. -- Even an electric-eye door can be regarded as a successful implementation of artificial intelligence. We skeptics' greatest doubts tend to focus on theories of emergent intelligence--theories as attractive to some modern researchers as the Philosopher's Stone was to medieval researchers, and (some say) with just as little basis in the nature of things. -- To divide intelligent beings into men and machines is not necessarily precise or exhaustive. For example, ghosts may be intelligent without belonging to either category. -- A secular equivalent of "godlike uniqueness" is that man is special: that we mean more to ourselves than does anything else, living or lifeless. Only a scientist would argue with this. 8 |-I ------------------------------ Date: 9 Oct 86 05:04:59 GMT From: allegra!princeton!mind!harnad@ucbvax.Berkeley.EDU (Stevan Harnad) Subject: Re: Turing test - the robot version >>> instead of a computer trying to fool you in ASCII, >>> it's a robot trying to fool you in the flesh... >>> Remember, scientists aren't just trying to make things better for you. >>> They're also trying to fool you! The purpose of scientific inquiry is not just to better the human condition. It is also to understand nature, including human nature. Nothing can do this more directly than trying to model the mind. But how can you tell whether your model is veridical? One way is to test whether its performance is identical with human performance. That's no guarantee that it's veridical, but there's no guarantee with our models of physical nature either. These too are underdetermined by data, as I argue in the papers in question. And besides, the robot version of the turing test is already the one we use every day, in our informal solutions to the other-minds problem. Finally, there's a world of difference, as likewise argued in the papers, between being able to "fool" someone in symbols and being able to do it in the flesh-and-blood world of objects and causality. And before we wax too sceptical about such successes, let's first try to achieve them. Stevan Harnad princeton!mind!harnad ------------------------------ Date: 10 Oct 1986 06:39 EDT (Fri) From: Wayne McGuire Subject: Understanding Horses Date: Mon 29 Sep 86 09:55:11-PDT From: Pat Hayes Subject: Searle's logic Look, I also don't think there's any real difference between a human's knowledge of a horse and [a] machine's manipulation of the symbol it is using to represent it. At one end of the human knowledge spectrum we have that knowledge of a horse which is aware that two horses + two horses = four horses; at the other end is that sort of rich and unfathomably complex knowledge which is expressed in a play like Peter Shaffer's _Equus_, and which fuses, under the force of sympathetic imagination, conceptual, emotional, biological, and sensorimotor modes of cognition. I suppose that our most advanced expert systems at the elementary end of the cognitive spectrum can capture knowledge about the structural and functional features of a horse, but it is not clear that any knowledge representation scheme will EVER simulate what is most interesting about human cognition and which relies on unconscious and intuitive resources. In one dimension of cognition the world is a machine, an engineering diagram, which is readily accessible by bit twiddling models; in another, that of, say, Shakespeare, it is a living organism, whose parts are infinitely interconnected and partially decrypted only by the power of the imagination. And so I would argue, with regard to human and machine cognition of horses or anything else, that there is a major difference in any dimension of knowledge that counts, and that repairing automobiles or space stations, and writing or understanding poems (or understanding the world in the broadest sense), have nearly nothing in common. Wayne McGuire (wayne@oz.ai.mit.edu) ------------------------------ Date: Fri, 10 Oct 86 11:57:31 edt From: Mike Tanner Reply-to: tanner@osu-eddie.UUCP (Mike Tanner) Subject: Re: Searle's logic Pat Hayes made some cogent remarks about Searle's problems with AI being much deeper than the discussion here would indicate. But I wonder whether the argument is worth the effort. I have a lot of work to do and only so much time. I can work just fine on problems of intelligence without worrying about Searle's (or Dreyfus's) complaints. Just as the working physicist can work all day without once being bothered by the question of whether quarks *really* exist, so the working AIer can make progress on his problems without being bothered by Searle. -- mike tanner@ohio-state.arpa ------------------------------ End of AIList Digest ******************** From csnet_gateway Wed Oct 15 14:35:37 1986 Date: Wed, 15 Oct 86 14:35:20 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #213 Status: R AIList Digest Tuesday, 14 Oct 1986 Volume 4 : Issue 213 Today's Topics: Query - Public Domain Software for Expert Systems, Expert Systems - Getting Started, AI Tools - Garbage Collection ---------------------------------------------------------------------- Date: 11 Oct 86 22:06:27 GMT From: ulysses!mhuxr!mhuxt!houxm!hou2d!meh@ucbvax.Berkeley.EDU (P.MEHROTRA) Subject: Public Domain Software for Expert Systems Public Domain Software for Expert Systems for building expert systems. I work in Unix environment and have Franz LISP on my system. I already have OPS5. I am especially interested in tools which use frames and/or semantic networks for knowledge representation. Any software or any information where I can get this software will be greatly appreciated. Prem K Mehrotra hou2d!meh speedy!prem 201-615-4535 ------------------------------ Date: 12 Oct 86 19:18:22 GMT From: well!jjacobs@LLL-LCC.ARPA (Jeffrey Jacobs) Subject: Getting started in Expert Systems > lem@galbp.UUCP > Lisa Meyer has requested information on expert systems, PD and PC related > tools. Lisa, I suggest that you start with Waterman's "A Guide to Expert Systems", as well as looking in your University book store and local commercial book stores and computer stores. This will give you a working bibliography to pursue. Most tools listed in the Waterman book are public domain and can often be obtained from the respective institution for a nominal price (usually of a tape). Periodicals include IEEE Expert (quarterly), AI Expert (monthly), SIGART (ACM Sig on AI), and AI Magazine (AAAI, quarterly). Also, see the July 86 issue of Computer (IEEE Computer Society). There are a number of PC tools and languages available. Best place to look is Byte magazine and various PC magazines. There are a number of LISPs, PROLOGs and a good Smalltalk available. TI has SCHEME and 2 levels of Personal Consultant. Insight-2+ has also received good reviews. For Public Domain PC software, I suggest the CompuServe Information Service (CIS). There is a Forum sponsored by AI Expert magazine which has a great deal of PD tools. It's also a great place for getting information oriented towards PC's. Also, the following BBS'es: Boston, Mass. (Common Lisp Group) (617) 492-2399 Woodbury, Conn. (203) 263-5783 Jeffrey M. Jacobs CONSART Systems Inc. Technical and Managerial Consultants P.O. Box 3016, Manhattan Beach, CA 90266 (213)376-3802 CIS:75076,2603 BIX:jeffjacobs USENET: well!jjacobs ------------------------------ Date: 6 Oct 86 02:38:00 GMT From: osiris!chandra@uxc.cso.uiuc.edu Subject: Re: Expert System Wanted There is no General Purpose expert system in the world. If you find one, you will probably get the Turing Award. I will be very happy to recieve more information about General Purpose Expert Systems. A breakthrough I am looking forward to. Please excuse my ignorance about this new technology. Thanks. Navin Chandra MIT ------------------------------ Date: 9 Oct 86 12:21:00 GMT From: osiris!chandra@uxc.cso.uiuc.edu Subject: Re: Expert System Wanted Hi, FOUND! There is an expert system shell for CMS. It is called PRISM. PRISM is also called ESE (expert system environemnt). It has production rule based programming and a interesting control structure based on Focus Control Blocks (with inheritance) ESE is available from IBM itself. It is written in lisp and was most probably developed at IBM Watson Research Labs. Navin Chandra MIT ------------------------------ Date: Mon, 6 Oct 86 10:02:17 cdt From: preece%ccvaxa@gswd-vms.ARPA (Scott E. Preece) Subject: Xerox vs Symbolics -- Reference coun > From: Dan Hoey > Let me first deplore the abuse of language by which it is claimed that > Xerox has a garbage collector at all. In the language of computer > science, Xerox reclaims storage using a ``reference counter'' > technique, rather than a ``garbage collector.'' This terminology > appears in Knuth's 1973 *Art of Computer Programming* and originated in > papers published in 1960. I remain undecided as to whether Xerox's > misuse of the term stems from an attempt at conciseness, ignorance of > standard terminology, or a conscious act of deceit. ---------- Hoey's pedantic insistence on a precision which does not exist in the "standard terminology" is apparently also an incorrect characterization of the Xerox approach, which [from the descriptions I have read] combines some aspects of the "pure" reference counting approach described by Knuth and some aspects of "pure" garbage collection. The Deutsch paper [CACM, 9/76] explicitly separates the two kinds of storage reclamation techniques and then proposes a combined method with features of both. In fact, however, the distinction on which Hoey places so much importance seems to have mostly vanished from the literature in the years since Knuth's description (why he places it in 1973 I don't know, my copy dates to 1968). Many more recent sources consider reference counting simply one form of garbage identification. The survey by Cohen (Computing Surveys, 9/81), for instance, discusses reference counting and marking as just two alternative ways of identifying garbage. Gabriel (Performance and Evaluation of Lisp Systems) says of the Xerox scheme, "Garbage collection is patterned after that described by [Deutsch, 1976]. A reference count is maintained..." Moon ("Garbage Collection in a Large Lisp System") discusses reference counting alternatives under the name garbage collection. Reference counting seems to have been accepted as a method of preforming one sub-task of garbage collection; Hoey's nit-picking is neither productive nor, since the Xerox approach is not pure reference counting, accurate. -- scott preece gould/csd - urbana uucp: ihnp4!uiucdcs!ccvaxa!preece arpa: preece@gswd-vms ------------------------------ Date: Mon, 6 Oct 86 10:05 EDT From: Scott Garren Subject: Garbage Collection Relative to discussions of garbage collectors I would like to point out that there are issues of scale involved. Many techniques that work admirably on an address space limited to 8 Mbytes (Xerox hardware) do not scale at all well to systems that support up to 1 Gbytes (Symbolics). Non-disclaimer: I am an employee of Symbolics and am of course emotionally and financially involved in this issue. ------------------------------ Date: Mon, 6 Oct 86 15:39:20 EDT From: ambar@EDDIE.MIT.EDU (Jean Marie Diaz) Reply-to: ambar@mit-eddie.UUCP (Jean Marie Diaz) Subject: Re: Xerox vs Symbolics -- Reference counts vs Garbage collection In article <8609262352.AA10266@ai.wisc.edu> neves@ai.wisc.edu (David M. Neves) writes: >Do current Symbolics users use the garbage collector? At MIT, yes. I do recall at Rutgers this summer that I was forever doing a (gc-on), because some user there was turning it off.... -- AMBAR "Timid entrant into the Rich Rosen School of Computer Learning...." ------------------------------ Date: Tue, 7 Oct 86 12:39:28 edt From: "Timothy J. Horton" Subject: Re: Xerox vs Symbolics -- Reference counts vs Garbage collection > When I was using MIT Lisp Machines (soon to become Symbolics) years > ago nobody used the garbage collector because it slowed down the > machine and was somewhat buggy. Instead people operated for hours/days > until they ran out of space and then rebooted the machine. The only > time I turned on the garbage collector was to compute 10000 factorial. > Do current Symbolics users use the garbage collector? > > "However, it is apparent that reference counters will never > reclaim circular list structure." > > This is a common complaint about reference counters. However I don't > believe there is very many circular data structures in real Lisp code. > Has anyone looked into this? Has any Xerox user run out of space > because of circular data structures in their environment? > > -- > David Neves, Computer Sciences Department, University of Wisconsin-Madison > Usenet: {allegra,heurikon,ihnp4,seismo}!uwvax!neves > Arpanet: neves@rsch.wisc.edu In the Xerox environment at least, the extensive use of windows is one of the most common sources of problems. Often is the case that a window must be 'related' somehow to another window i.e. you create a subsidiary window for some main window (as a scroll window is to a display window), and the two windows must 'know' about each other. The obvious thing is to put pointers on each window's property list to the other window, "et viola" a circular list. Everything on the property lists of the two windows also gets kept around, and since Xerox windows are such good places to store things the circular structure is often very large. (check out the stuff on a 'Sketch' window's property list) A careful programmer can avoid such problems. In the case of windows, one just has to be careful about how windows find out about one another (some kind of global variable scheme or a directed search of all windows). Yet accidents happen and windows can kill the environment fairly quickly. Yes, I have lost an environment to just this problem (that's why I know), and it's very hard to tell what happened after the fact. ------------------------------ Date: Fri 10 Oct 86 10:57:53-PDT From: Keith Price Subject: Garbage collection The experience in our lab is that there is no garbage collection other than the Ephemeral GC; the "traditional" GC is never needed or executed and programs are faster with the Ephemeral GC on than with it off. I can't say whether it is "better" than Xerox or the new LMI GC, but it is clear that "traditional" GC is a thing of the past for most Lisp work stations already and comparisons to such old methods do not contribute to the knowledge pool. K. Price. price%ganelon@usc-ecl ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:10:44 1986 Date: Mon, 20 Oct 86 03:10:34 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #214 Status: R AIList Digest Tuesday, 14 Oct 1986 Volume 4 : Issue 214 Today's Topics: Query - Connectionist References, Applications - Animal Rule-Systems Simulations, Expert Systems - Coupling Numeric and Symbolic Computing, Survey - Interactive 2-D Math Editing Interfaces ---------------------------------------------------------------------- Date: Tue, 14 Oct 86 14:48:20 nzs From: ubc-vision!calgary!vuw90x!paul@seismo.CSS.GOV (Paul Fitchett) Reply-to: ubc-vision!vuwcomp!paul@seismo.CSS.GOV (Paul Fitchett) Subject: Connectionist References Request Recent items in mod.ai have piqued my interest about "connectionist" ideas in AI. I wonder if anyone could provide a number of references that are a good introduction to the ideas in this area. The little I've read makes them seem like perceptrons -- I hope not. If replying by email please use one of the paths below. Thanks, Paul Paul Fitchett uucp : ...!{ubc-vision, alberta}!calgary!vuwcomp!paul ACSnet : paul@vuwcomp.nz subethernet : ...local-group!milky-way!sol!terra!nz!vuwcomp!paul :-) ------------------------------ Date: 13 Oct 86 18:14:19 GMT From: ucdavis!deneb!g451252772ea@ucbvax.Berkeley.EDU (g451252772ea) Subject: animal rule-systems simulations By way of introduction to the following Mail message, 'bc' posted last spring a query for anyone with references on 'simulation animal behavior using rule-driven systems'. I discovered his message in an old listing, and find the topic of interest also. In case 'bc' (William Coderre) is no longer at mit-amt.MIT.EDU, I'm posting to the net also. Thanks for your tolerance... Hi, bc (?bc?): I'm curious about any replies you got to your query last April for rule-driven simulations of animal behaviors. I have somewhat similar interests, reflecting my grad work in ethology here at Davis, and my undergrad work at U.C. Santa Cruz in Information- Computer science. We have here a person doing stuff you'd enjoy: Marc Mangel, with his 'dynamic stochastic optimization' analysis of everything from insect oviposition choices to foraging theory to fisheries harvesting. His insight seems to be the addition of a 'state variable' - usually characterized as energy reserves, gut contents or similar - to revamp the static optimization models of Houston, McNamara, Krebs, et al. (the 'Oxford' crowd). Mangel is chair of the math dept. here, and co-authors with Colin Clark of the U. British Columbia. Clark is visiting here this quarter and giving an applied math seminar, with lots of application studies. Both guys emphasize computer programs, and the programs have a game-like air to them. If you'd like more info, I can send some typed notes by Mangel describing the analysis, and one of his most counter-intuitive applications. Mostly I'm working with evolutionary studies: the predator/prey interactions of snakes and ground squirrels (my thesis is on stupidity: the dumbness of Arctic ground squirrels, which don't even appear to snakes of any kind, much less handle them correctly). I do have to give a week's worth of lectures to my animal-behavior group next month, explaining 'artificial intelligence' ab initio to them. Despite Mangel and Clark, the prejudice against math/systems here is substantial. Any ideas you have for good material/examples, in the vein of Winograd's new book or Rosen's discussion of ANTICIPATORY SYSTEMS (much watered down!) or ANYTHING else, would be most welcome! Thanks --Ron. ------------------------------ Date: Fri 10 Oct 86 15:00:40-EDT From: Albert Boulanger Subject: Expert Systems & Math Models Check out the book: Coupling Symbolic and Numerical Computing in Expert Systems Edited by J.S. Kowalik and is based on the workshop by this name held at Bellevue, Washington 27-29 August, 1985. Elsevier, 1986 ------------------------------ Date: 0 0 00:00:00 PDT From: "LLLASD::GARBARINI" Reply-to: "LLLASD::GARBARINI" Subject: RE: Availability of interactive 2-d math editing interfaces... The following is a summary of responses to my query on 2-d math editing interfaces. I'd like to thank everyone who responded. I hope to eventually respond to each of you individually. ---------- Joe P. Garbarini Jr. Lawrence Livermore National Lab P. O. Box 808 , L-308 7000 East Avenue Livermore Ca. , 94550 arpanet address: GARBARINI%LLLASD.DECNET@LLL-ICDC.ARPA ---------- The original query: ----- I am working with a number of other people on a project called Automatic Programming for Physics. The goal is to build an AI based automatic programming system to aid scientist in the building of numerical simulations of physical systems. In the user interface to the system we would like to have interactive editing of mathematical expressions in two-dimensional form. It seems a number of people have recently made much progress in this area. (See C. Smith and N. Soiffer, "MathScribe: A User Interface for Computer Algebra Systems," Conference Proceedings of Symsac 86, (July, 1986) and B. Leong, "Iris: Design of a User Interface Program for Symbolic Algebra," Proc. 1986 ACM-SIGSAM Symposium on Symbolic and Algebraic Manipulation, July 1986.) Not wishing to reinvent the wheel, I'd appreciate receiving information regarding the availability of any such interface. ======================================================================= From: Joe Garbarini (Yes, this is from me!) MathSoft makes a product call MathCAD which has an interactive 2-d math interface. Currently runs on IBM PCs. Mathsoft, Inc. One Kendal Square, Bldg. 100 Cambridge, MA 02139 800-628-4223 ----- From: James E. O'Dell Normal MACSYMA has 2-d editing done by Carl Hoffman and Rich Zippel. I think references to it can be found in one or the other of the Proceedings of the MACSYMA Users Group. Jim ----- From: fateman@dali.Berkeley.EDU (Richard Fateman) There is some stuff on Sun-2 equipment working with macsyma here at UC Berkeley. The MathScribe stuff is currently nicer looking in my opinion, but people are still working on stuff here. ----- A version of Macsyma for the VAX computer, including sources and binaries for Macsyma and the underlying Lisp (Franz Lisp opus 38.91), is in the National Energy Software Center library. (Argonne, IL.) (312) 972-7172. This should run without change on 4.3BSD UNIX or ULTRIX. This version, which was developed at the University of California, has also been run, with modifications, on various other (non-VAX) systems which support the Franz Lisp dialect. For information on Franz Lisp for VAX/VMS or other computers, you might wish to contact your hardware vendor or Franz Inc. in Alameda CA, (415) 769-5656. (for many mainframe and workstation computers) Vaxima uses about 4.5 megabytes of address space to start up, and as configured, can grow to 6.5 megabytes or so. By changing a compile-time parameter in the Lisp system, the system may be configured to grow much larger. (We have run a 53 megabyte system on a VAX 8600). At UC Berkeley we have been using this code on Sun-2 and Sun-3 systems, and microVAX-II's. Vaxima is quite fast when given enough physical memory, and appears at this time to be very cost-effective compared to implementations on special-purpose Lisp machines or "DOE-MACSYMA" for VAX/VMS. There are a number of packages that have been developed to work with this program (e.g. user interfaces, better algorithms for factoring, graphics, an interface to the Numerical Algorithms Group (NAG) library). Neither UC Berkeley nor by NESC provides support for vaxima. Richard J. Fateman, U.C. Berkeley ----- From: arnon.pa@xerox.com For some years Xerox has offered software for interactive two-dimensional editing of mathematical expressions as part of its Star, and now Viewpoint, systems. Regrettably Viewpoint runs only on Xerox workstations. As part of the research programming environment at Xerox PARC, we have a more powerful math editing and display package which is roughly the equivalent of MathScribe. A noteworthy property of our package is that a math expression can be moved interchangeably between the editor, a technical document, and a system for symbolic mathematical computation. I'd be happy to discuss or demo. Dennis Arnon Computer Science Laboratory Xerox Palo Alto Research Center 3333 Coyote Hill Road Palo Alto CA 94304 (415) 494-4425 ----- From: seismo!Xerox.COM!Kelley.pa While not oriented specifically to physical systems, STELLA for the Macintosh from High-Performance Systems, Inc., 13 Dartmouth College Highway, Lyme, New Hampshire 03768. has a very nice user interface for help in building mathematical models. It is oriented toward Forrester's Systems Dynamics simulation paradigm. The user interface is worth examining. -- kirk ----- From: mcgeer%sirius.Berkeley.EDU@BERKELEY.EDU (Rick McGeer) soiffer@dali.berkeley.edu carolyn@dali.berkeley.edu are the addresses of Neil Soiffer and Carolyn Smith, respectively. Benton Leong is at allegra!watmath!blleong -- Rick ----- From: Doug A. Young Tony Hearn forwarded a message from you a while back to me, asking about an interface for algebra systems. I did my masters thesis on a graphical multi-window system for Macsyma. If you are interested, I could forward some information on it to you. Contact me at dayoung@hplabs Doug Young ----- From: Bill Schelter I have an emacs like editor which allows the display of mathematics as well as textual material. You can mouse into the superscript position etc. The system is called INFOR, and is available for lisp machines (ymbolics and TI I also ported the version of macsyma from doe, to run on those machines. It would be fairly easy to connect the two, since they are running in the same memory space. The display is very good, at least as good as TEX. You can actually create a dvi file directly from the editor. This can be then printed to obtain very high quality output. Bill Schelter ------- From: mcvax!fransh@seismo.CSS.GOV (Frans Heeman) Some while ago, you put a query on the news about the availability of interactive 2-d math editing interfaces. We are working on a formula-editor (NOT a formula-manipulator). The idea is as follows: For example, the user gives the command for a fraction to be entered. On the screen a small horizontal bar is displayed, and the cursor is positioned above this bar. The user types in the nominator. While typing, the fraction-bar remains as long as the nominator. Next the user gives the END-command, to indicate the end of the nominator. Now the cursor is centered under the fraction-bar, and the user types in the denominator. While typing, the nominator and denominator remain centered with respect to the fraction-bar, and the fraction-bar remains as long as the longest of the nominator and denominator. By means of keyboard and mouse (menu's) the user can enter a mathematical formula. While typing, the formula is at every moment displayed on the screen in its current 2-dimensional form. The system can handle mathematical constructs as fraction, root, integral, matrices, etc. The system also handles greek and italic characters. The constructs the system can handle are specified in an external grammar, so it is relatively easy to add or change constructs. The way the formula is displayed on the screen is also specified in this grammar. It is possible to get a hard-copy of the formula: this is done by generating 'eqn'-code for the formula, and then get a typeset result by using 'eqn' and 'troff' (part of the UNIX operating system). Finally, a formula can be saved, retrieved and edited. Our system is still under development and not yet available for 'real' use. The final goal is to make a document-editor for text, tables, mathematical formulae and (simple) pictures. In France, Vincent Quint et al is doing work along these lines too with a system previously called 'Edimath', currently called 'Grif'. References: We have only published an internal report (in dutch), and are currently preparing an English article to be published. Edimath: V. Quint (March 1983), "An Interactive System for Mathematical Text Processing", Technology and Science of Informatics, vol. 2, nr. 3, pp. 169-179. Grif: V. Quint, I. Vatton (April 1986), "Grif: An Interactive System for Structured Document Manipulation", Proceedings of the Conference on Text Processing and Document Manipulation, Nottingham, England, 1986. Frans C. Heeman Centre for Mathematics and Computer Science (CWI) P. O. Box 4079 1009 AB Amsterdam The Netherlands fransh@mvcax.UUCP ------------------------------ End of AIList Digest ******************** From csnet_gateway Wed Oct 15 14:36:20 1986 Date: Wed, 15 Oct 86 14:35:59 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #215 Status: R AIList Digest Tuesday, 14 Oct 1986 Volume 4 : Issue 215 Today's Topics: Binding - David Etherington, Review - Spang Robinson Report, October 1986, Survey - Intelligent Tutoring Systems ---------------------------------------------------------------------- Date: Tue 7 Oct 1986 16:24:42 From: ether.allegra%btl.csnet@CSNET-RELAY.ARPA Subject: Binding David Etherington, from the University of British Columbia to AT&T Bell Laboratories, AI Principles Research Department. Addresses: ether%allegra@btl.csnet and David W. Etherington, AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, NJ, 07974-2070 ------------------------------ Date: WED, 20 apr 86 17:02:23 CDT From: E1AR0002%SMUVM1.BITNET@WISCVM.WISC.EDU Subject: Spang Robinson Report, October 1986 Summary of Volume 2 No 10 Discussion of S.1, ART, KEE discussing user interface, performance, features. A common problem was done in all three applications. The person who did the evaluation started the exercise with the impression that one was best off starting in scratch. This was changed after the evaluation was completed. Also includes a two page table giving features, operating system and cost for various expert system building tools including micro-based tools __________________________________________________________________________ Japan Watch MITI has budgeted the following for AI type products $400,000 diagnosis support systems 2.4 million robotics 1.1 million language translation systems $234,000 factory automation R&D The Patent Office has budgeted $162,330 for machine translation The National Police Agency will be putting $150,000 on automated voice recognition $84,400 for recognition systems $84,400 for graphology systems The National Agency of Science and Technology budged 1.9 million on a machine translation system The Ministry of Agriculture and Foresty is doing research on expert systems in agricultural production control __________________________________________________________________________ Short Notes Cognitive Systems quarterly revenues were $585,000 with a loss of $31,000 Frey Associates which sells THEMIS, a natural language product, made $102,820 on revenues of $1,408,529 in the last quarter Intellicorp expects a substantial loss this quarter. Dr. Robert Moore, previously of Lisp Machine Co, is now president of GENSYM, which anticipates doing work in real time applications of AI. Eloquent Systems Corporations has produced an expert system for hotels, motels, etc. to optimize occupancy and profits. It runs on an Explorer with a special card for multi processing. Sperry has developed expert systems for configuring shipboard software systems, a Tactical Information System for monitoring reports of ship and aircraft locations, testing PC boards, correlating contact reports for the NAVY and a system software diagnosis system. Sanders Associates is undertaking a Defense Department Study to set standards for developing AI systems. DOD expects to release a set of AI software development standards within three to five years. __________________________________________________________________________ Reviews of Applications of Artificial Intelligence III, proceedings of SPIE's conference in Orlando Expert System 85, Fifth Technical Conference of the British Computer Society's Sppecialists Group on Expert Systems Artificial Intelligence and Statistics, which includes most of the Articles of Workshop on Artifiicial Intelligence and Statistics, April 1985 Artificial Intelligence with Statistical Pattern Recognition Lisp Lore: A Guide to Programming the LISP Machine ------------------------------ Date: Sun, 28 Sep 86 00:49:35 BST From: YAZDANI%UK.AC.EXETER.PC@AC.UK Subject: A survey of prototype and working ITSs. [Forwarded from the AI-Ed digest by Laws@SRI-STRIPE.] Here I present a survey of Intelligent Tutoring systems which, although not exhaustive, is intended to be a source of reference for further development. I would like to know of other systems which I should add, prefably getting enteries in my proposed format. However, if you can't spend the time to do this if you just send me any references you may have I shall try and extract the information myself. Also if you would like to suggest changes to the format to make it more useful please do so. I would like to send the final version to somewhere( like AI mag. for publication) and shall acknowledge any help I get. You can't use REPLY to get to me so you need to SEND me Email to YAZDANI%UK.AC.EXETER.PC@UCL-CS.arpa or post to Dept. of Computer Sceince University of Exeter Prince of Wales Road EXETER EX4 4PT ENGLAND Thanks ______ ACE Subject: Nuclear Magnetic Spectroscopy Aim: Monitor Deductive Reasonsing Features: Problem solving monitor, accepts natural language input System: MODULAR ONE Reference: Sleeman, D.H., and Hendley, R. J. (1982) ACE: a system which analyses complex explanations in Sleeman and Brown (eds.) BUGGY & DEBUGGY Subject: Arithmetic Aim: Diagnose bugs from behaviour Features: Procedural representation of misconceptions (bugs), hypothesis generation, problem generation system: LISP System: LISP Reference: Brown, R.R. (1982) Diagnosing bugs in simple procedural skills in Sleeman & Brown (eds.) BLOCKS Subject: Blocks game Aim: Diagnosis System: LISP Reference: Brown, J.S. and Brown, R. R. (1978) "A paradigmatic example of an artificially intelligent instructional system" Int. J. of Man-Machine Studies Vol., 10, pp.232-339. FGA Subject: French Grammar Aim: Analyse free form French sentences Features: Separation of dictionary, grammar, parser and error reporting, general shell idea, human controlled teaching strategy System: PROLOG Reference: Barchan, J. Woodmansee, B.J. and Yazdani, M. (1985) "A Prolog-based tool for French Grammar Analysis" Instructional Science, Vol. 14 GUIDON Subject: Medical diagnosis Aim: Using MYCIN for tutoring Features: Overlay student model, case method, separate of domain knowledge from teaching expertise System: LISP Reference: Clancey, W.J. (1979) Tutoring rules for guiding a case method dialogue in Int. J. of Man-Machine Studies, Vol. 11 pp 25-49. GEOMETRY Tutor Subject: Geometry Aim: Monitoring geometry proof problems Features: Use of production rules to represent 'ideal student model' and 'bug catalogue' System: Franz LISP Reference: Anderson, J.R., Boyle, C.F. and Yost, G. The Geometry Tutor Proceedings of IJCAI-85 INTEGRATION Subject: Calculus Aim: To deal with student initiated examples of symbolic integration Features: Self-improvement System: LISP Reference: Kimbal, R. (1982) A self-improving tutor for symbolic integration in Sleeman and Brown (eds.) LISP Tutor Subject: LISP programming Aim: Teaching of introductory LISP programming Features: Using deviation from ideal student model System: Franz LISP on VAX Reference: Anderson, J.R. and Reiser, B. (1985) The LISP Tutor in Byte Vol. 10 No. 4 LMS (Pixie) Subject: Algebra equetion solving Aim: Building student models Features: Given problems and students answers it hypothesizes models for them; uses rules and mal-rules. System: LISP Reference: Sleeman, D.A. (1983) Inferring student models for intelligent computer-aided instruction in Michalsky, R. Carbonnel, J. and Mitchell, T. (eds.) Machine Learning Springer-Verlag/Toga Press MENO Subject: Pascal programming Aim: Tutoring novice programmers in the use of planning Features: Hierarchical representation of correct and incorrect plans System: LISP Reference: Woolf, B. and McDonald, D.D.(1984) Building a computer tutor: design issues IEEE Computers Sept. issue, pp. 6l-73 MACSYMA ADVISOR Subject: Use of MACSYMA Aim: Articulate users misconceptions about MACSYMA Features: Representation of plans System: LISP Reference: Genesreth, M.R. (1977) An automated consultant for MACSYMA Proceedings of IJCAI-77 NEOMYCIN Subject: Medical diagnosis Aim: Using expert systems for tutoring Features: Separate of domain knowledge from teaching expertise, automatic explanation of experts' reasoning System: LISP Reference: Hasling, D.W., Clancey, W.J. and Rennels, G. (1984) Strategic explanations for a dioagnostic consultation system PROUST Subject: Pascal programming Aim: Automatic debugger and tutor Features: Use of problem descriptions System: GCL LISP on IBM PC (micro-PROUST), LISP on VAXs Reference: Johnson, W. L. and Soloway, E. (1985) PROUST in Byte Vol. 10, No. 4. QUADRATIC tutor Subject: Calculus Aim: Teaching quadrantic equations Features: Teaching strategy represented as a set of production rules System: LISP Reference: O'Shea, T. (1982) A self-improving quadratic tutor in Sleeman and Brown (eds.) Scholar Subject: Geography Aim: Provide mixed-initiative dialogue Features: Semantic network representation of knowledge System: LISP Reference: Carbonnel, J.R. and Collins, A. (1973) "Natural Semantics in Artificial Intelligence" Proceedings of IJCAI-73 Proceedings of IJCAI-85 SOPHIE Subject: Electronic trouble shooting Aim: Teaching how an expert trouble shooter copes with rare faults Features: Semantic grammar for natural language diaglogue, qualitative knowledge plus simulation, multiple knowledge sources System: LISP Reference: Brown, J.S., Burton, R. R. and de Kleer, J. (1982) "Pedagogical, natural language and knowledge engineering techniques in SOPHIE I, II and III in Sleeman, D. and Brown, J.S. (eds.)B SPADE Subject: LOGO programming Aim: To facilitate the acquisition of programming skills Features: Intelligent editor which prompts the student with menu of design alternatives Reference: Miller, M.L. (1982)) A Structured Planning and Debugging Environment Inferring student models for intelligent computer-aided in Sleeman and Brown (eds.) STEAMER Subject: Steam plant operation Aim: Convey qualitative model of a steam plant operation Features: Good graphics and mathematical model of the plant System: LISP Reference: Holland, J.D., Hutchins, E. L. and Weitzmann, L. (1984) "STEAMER: An interative inspectable simutation based training system" in The AI Magazine, Vol. 5. No. 2 TUTOR Subject: Highway Code Aim: Prototype framework for a wide variety of subjects Features: Semantic grammar implemented in definite clause grammar, representing value clusters, "what if" facility System: Prolog on VAX and IBM PC AT Reference: Davies, N., Dickens, S. and Ford, L. (1985) "TUTOR": A prototype ICAI system" in M. Bramer (ed.) 'Research and Development in Expert Systems' Cambridge University Press WEST Subject: How the West was Won Aim: Drill and Practice in arithmetic Features: Hierarchical representation of correct and incorrect plans System: PLATO Reference: Comparison of students' moves with experts' moves, student model and diagnostic strategies, tutoring expert WHY Subject: Meteorology Aim: Tutoring students about processes involved in rainfall Features: Multiple representations in direct tuition System: LISP Reference: Stevens, A. and Goldin, S. F. (1982) Misdconceptions in student understanding in Sleeman and Brown (eds.) WUSOR Subject: Maze exploration game (Wumpus) Aim: Teaching logic and probability Features: Graph structure whose nodes represent rules System: LISP Reference: Goldstein, I. (1982) "The genetic graph: A representation for evlution of procedural knowledge" in Sleeman and Brown (eds.) ------------------------------ End of AIList Digest ******************** From csnet_gateway Fri Oct 17 02:13:53 1986 Date: Fri, 17 Oct 86 02:13:45 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #216 Status: R AIList Digest Thursday, 16 Oct 1986 Volume 4 : Issue 216 Today's Topics: Philosophy - Searle, Turing, Symbols, Categories ---------------------------------------------------------------------- Date: 9 Oct 86 15:23:35 GMT From: cbatt!ukma!drew@ucbvax.Berkeley.EDU (Andrew Lawson) Subject: Re: Searle, Turing, Symbols, Categories (Question not comment) In article <160@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes: > >On my argument the distinction between the two versions is critical, >because the linguistic version can (in principle) be accomplished by >nothing but symbols-in/symbols-out (and symbols in between) whereas >the robotic version necessarily calls for non-symbolic processes >(transducer, effector, analog and A/D). This is not clear. When I look at my surroundings, you are no more than a symbol (just as is anything outside of my being). Remember that "symbol" is not rigidly defined most of the time. When I recognize the symbol of a car heading toward me, I respond by moving out of the way. This is not essentially different from a linguistic system recognizing a symbol and responding with another symbol. -- Drew Lawson cbosgd!ukma!drew "Parts is parts." drew@uky.csnet drew@UKMA.BITNET ------------------------------ Date: 6 Oct 86 18:15:42 GMT From: mnetor!utzoo!utcsri!utai!me@seismo.css.gov (Daniel Simon) Subject: Re: Searle, Turing, Symbols, Categories (Question not comment) In article <160@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes: > >In reply to (1): The linguistic version of the turing test (turing's >original version) is restricted to linguistic interactions: >Language-in/Language-out. The robotic version requires the candidate >system to operate on objects in the world. In both cases the (turing) >criterion is whether the system can PERFORM indistinguishably from a human >being. (The original version was proposed largely so that your >judgment would not be prejudiced by the system's nonhuman appearance.) > I have no idea if this is a relevant issue or a relevant place to bring it up, but this whole business of the Turing test makes me profoundly suspicious. For example, we all know about Weizenbaum's ELIZA, which, he claimed, convinced many clever, relatively computer-literate (for their day) people that it was intelligent. This fact leads me to some questions which, in my view, ought to be seriously addressed before the phrase "Turing test" is bandied about (and probably already have been addressed, but I didn't notice, and will thank everybody in advance for telling me where to find a treatment of them and asking me to kindly buzz off): 1) To what extent is our discernment of intelligent behaviour context- dependent? ELIZA was able to appear intelligent because of the clever choice of context (in a Rogerian therapy session, the kind of dull, repetitive comments made by ELIZA seem perfectly appropriate, and hence, intelligent). Mr. Harnad has brought up the problem of physical appearance as a prejudicing factor in the assessment of "human" qualities like intelligence. Might not the robot version lead to the opposite problem of testers being insufficiently skeptical of a machine with human appearance (or even of a machine so unlike a human being in appearance that mildly human-like behaviour takes on an exaggerated significance in the tester's mind)? Is it ever possible to trust the results of any instance of the test as being a true indicator of the properties of the tested entity itself, rather than those of the environment in which it was tested? 2) Assuming that some "neutral" context can be found which would not "distort" the results of the test (and I'm not at all convinced that such a context exists, or even that the idea of such a context has any meaning), what would be so magic about the level of perceptiveness of the shrewdest, most perspicacious tester available, that would make his inability to distinguish man from machine in some instance the official criterion by which to judge intelligence? In short, what does passing (or failing) the Turing test really mean? 3) If the Turing test is in fact an unacceptable standard, and building a machine that can pass it an inappropriate goal (and, as questions 1 and 2 have probably already suggested, this is what I strongly suspect), are there more appropriate means by which we could evaluate the human-like or intelligent properties of an AI system? In effect, is it possible to formulate the qualities that constitute intelligence in a manner which is more intuitively satisfying than the standard AI stuff about reasoning, but still more rigorous than the Turing test? As I said, I don't know if my questions are legitimate, or if they have already been satisfactorily resolved, or if they belong elsewhere; I merely bring them up here because this is the first place I have seen the Turing test brought up in a long time. I am eager to see what others have to say on the subject. >Stevan Harnad >princeton!mind!harnad Daniel R. Simon "Look at them yo-yo's, that's the way to do it Ya go to grad school, get your PhD" ------------------------------ Date: 10 Oct 86 13:47:46 GMT From: rutgers!princeton!mind!harnad@think.com (Stevan Harnad) Subject: Re: Searle, Turing, Symbols, Categories In response to what I wrote in article <160@mind.UUCP>, namely: >On my argument the distinction between the two versions >[of the turing test] is critical, >because the linguistic version can (in principle) be accomplished by >nothing but symbols-in/symbols-out (and symbols in between) whereas >the robotic version necessarily calls for non-symbolic processes >(transducer, effector, analog and A/D). Drew Lawson replies: > This is not clear. When I look at my surroundings, you are no > more than a symbol (just as is anything outside of my being). > Remember that "symbol" is not rigidly defined most of the time. > When I recognize the symbol of a car heading toward me, I respond > by moving out of the way. This is not essentially different from > a linguistic system recognizing a symbol and responding with another > symbol. It's important, when talking about what is and is not a symbol, to speak literally and not symbolically. What I mean by a symbol is an arbitrary formal token, physically instantiated in some way (e.g., as a mark on a piece of paper or the state of a 0/1 circuit in a machine) and manipulated according to certain formal rules. The critical thing is that the rules are syntactic, that is, the symbol is manipulated on the basis of its shape only -- which is arbitrary, apart from the role it plays in the formal conventions of the syntax in question. The symbol is not manipulated in virtue of its "meaning." Its meaning is simply an interpretation we attach to the formal goings-on. Nor is it manipulated in virtue of a relation of resemblance to whatever "objects" it may stand for in the outside world, or in virtue of any causal connection with them. Those relations are likewise mediated only by our interpretations. This is why the distinction between symbolic and nonsymbolic processes in cognition (and robotics) is so important. It will not do to simply wax figurative on what counts as a symbol. If I'm allowed to use the word metaphorically, of course everything's a "symbol." But if I stick to a specific, physically realizable sense of the word, then it becomes a profound theoretical problem just exactly how I (or any device) can recognize you, or a car, or anything else, and how I (or it) can interact with such external objects robotically. And the burden of my paper is to show that this capacity depends crucially on nonsymbolic processes. Finally, apart from the temptation to lapse into metaphor about "symbols," there is also the everpresent lure of phenomenology in contemplating such matters. For, apart from my robotic capacity to interact with objects in the world -- to recognize them, manipulate them, name them, describe them -- there is also my concsiousness: My subjective sense, accompanying all these capacities, of what it's like (qualitatively) to recognize, manipulate, etc. That, as I argue in another paper (and only hint at in the two under discussion), is a problem that we'd do best to steer clear of in AI, robotics and cognitive modeling, at least for the time being. We already have our hands full coming up with a model that can successfully pass the (robotic and/or linguistic) turing test -- i.e., perform exactly AS IF it had subjective experiences, the way we do, while it successfully accomplishes all those clever things. Until we manage that, let's not worry too much about whether the outcome will indeed be merely "as if." Overinterpreting our tools phenomenologically is just as unproductive as overinterpreting them metaphorically. Stevan Harnad princeton!mind!harnad ------------------------------ End of AIList Digest ******************** From csnet_gateway Fri Oct 17 02:14:12 1986 Date: Fri, 17 Oct 86 02:14:00 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #217 Status: R AIList Digest Thursday, 16 Oct 1986 Volume 4 : Issue 217 Today's Topics: Philosophy - Searle, Turing, Symbols, Categories ---------------------------------------------------------------------- Date: 10 Oct 86 15:50:33 GMT From: rutgers!princeton!mind!harnad@lll-crg.arpa (Stevan Harnad) Subject: Re: Searle, Turing, Symbols, Categories In response to my article <160@mind.UUCP>, Daniel R. Simon asks: > 1) To what extent is our discernment of intelligent behaviour > context-dependent?...Might not the robot version [of the > turing test] lead to the...problem of testers being > insufficiently skeptical of a machine with human appearance? > ...Is it ever possible to trust the results of any > instance of the test...? My reply to these questions is quite explicit in the papers in question: The turing test has two components, (i) a formal, empirical one, and (ii) an informal, intuitive one. The formal empirical component (i) is the requirement that the system being tested be able to generate human performance (be it robotic or linguistic). That's the nontrivial burden that will occupy theorists for at least decades to come, as we converge on (what I've called) the "total" turing test -- a model that exhibits all of our robotic and lingistic capacities. The informal, intuitive component (ii) is that the system in question must perform in a way that is indistinguishable from the performance of a person, as judged by a person. It is not always clear which of the two components a sceptic is worrying about. It's usually (ii), because who can quarrel with the principle that a veridical model should have all of our performance capacities? Now the only reply I have for the sceptic about (ii) is that he should remember that he has nothing MORE than that to go on in the case of any other mind than his own. In other words, there is no rational reason for being more sceptical about robots' minds (if we can't tell their performance apart from that of people) than about (other) peoples' minds. The turing test is ALREADY the informal way we contend with the "other-minds" problem [i.e., how can you be sure anyone else but you has a mind, rather than merely acting AS IF it had a mind?], so why should we demand more in the case of robots? It's surely not because of any intuitive or a priori knowledge we have about the FUNCTIONAL basis of our own minds, otherwise we could have put those intuitive ideas to work in designing successful candidates for the turing test long ago. So, since we have absolutely no intuitive idea about the functional (symbolic, nonsymbolic, physical, causal) basis of the mind, our only nonarbitrary basis for discriminating robots from people remains their performance. As to "context," as I argue in the paper, the only one that is ultimately defensible is the "total" turing test, since there is no evidence at all that either capacities or contexts are modular. The degrees of freedom of a successful total-turing model are then reduced to the usual underdetermination of scientific theory by data. (It's always possible to carp at a physicist that his theoretic model of the universe "is turing-indistinguishable from the real one, but how can you be sure it's `really true' of the world?") > 2) Assuming that some "neutral" context can be found... > what does passing (or failing) the Turing test really mean? It means you've successfully modelled the objective observables under investigation. No empirical science can offer more. And the only "neutral" context is the total turing test (which, like all inductive contexts, always has an open end, namely, the everpresent possibility that things could turn out differently tomorrow -- philosophers call this "inductive risk," and all empirical inquiry is vulnerable to it). > 3) ...are there more appropriate means by which we > could evaluate the human-like or intelligent properties of an AI > system? ...is it possible to formulate the qualities that > constitute intelligence in a manner which is more intuitively > satisfying than the standard AI stuff about reasoning, but still > more rigorous than the Turing test? I don't think there's anything more rigorous than the total turing test since, when formulated in the suitably generalized way I describe, it can be seen to be identical to the empirical criterion for all of the objective sciences. Residual doubts about it come from four sources, as far as I can make out, and only one of these is legitimate. The legitimate one (a) is doubts about autonomous symbolic processes (that's what my papers are about). The three illegitimate ones (in my view) are (b) misplaced doubts about underdetermination and inductive risk, (c) misplaced hold-outs for the nervous system, and (d) misplaced hold-outs for consciousness. For (a), read my papers. I've sketched an answer to (b) above. The quick answer to (c) [brain bias] -- apart from the usual structure/function and multiple-realizability arguments in engineering, computer science and biology -- is that as one approaches the asymptotic Total Turing Test, any objective aspect of brain "performance" that anyone believes is relevant -- reaction time, effects of damage, effects of chemicals -- is legitimate performance data too, including microperformance (like pupillary dilation, heart-rate and perhaps even synactic transmission). I believe that sorting out how much of that is really relevant will only amount to the fine-tuning -- the final leg of our trek to theoretic Utopia, with most of the substantive theoretical work already behind us. Finally, my reply to (d) [mind bias] is that holding out for consciousness is a red herring. Either our functional attempts to model performance will indeed "capture" consciousness at some point, or they won't. If we do capture it, the only ones that will ever know for sure that we've succeeded are our robots. If we don't capture it, then we're stuck with a second level of underdetermination -- call it "subjective" underdetermination -- to add to our familiar objective underdetermination (b): Objective underdetermination is the usual underdetermination of objective theories by objective data; i.e., there may be more than one way to skin a cat; we may not happen to have converged on nature's way in any of our theories, and we'll never be able to know for sure. The subjective twist on this is that, apart from this unresolvable uncertainty about whether or not the objective models that fit all of our objective (i.e., intersubjective) observations capture the unobservable basis of everything that is objectively observable, there may be a further unresolvable uncertainty about whether or not they capture the unobservable basis of everything (or anything) that is subjectively observable. AI, robotics and cognitive modeling would do better to learn to live with this uncertainty and put it in context, rather than holding out for the un-do-able, while there's plenty of the do-able to be done. Stevan Harnad princeton!mind!harnad ------------------------------ Date: 12 Oct 86 19:26:35 GMT From: well!jjacobs@lll-lcc.arpa (Jeffrey Jacobs) Subject: Searle, AI, NLP, understanding, ducks I. What is "understanding", or "ducking" the issue... If it looks like a duck, swims like a duck, and quacks like a duck, then it is *called* a duck. If you cut it open and find that the organs are something other than a duck's, *then* maybe it shouldn't be called a duck. What it should be called becomes open to discussion (maybe dinner). The same principle applies to "understanding". If the "box" performs all of what we accept to be the defining requirements of "understanding", such as reading and responding to the same level as that of a "native Chinese", then it certainly has a fair claim to be called "understanding". Most so-called "understanding" is the result of training and education. We are taught "procedures" to follow to arrive at a desired result/conclusion. The primary difference between human education and Searle's "formal procedures" is a matter of how *well* the procedures are specified . Education is primarily a matter of teaching "procedures", whether it be mathematics, chemistry or creative writing. The *better* understood the field, the more "formal" the procedures. Mathematics is very well understood, and consists almost entirely of "formal procedures". (Mathematics was also once considered highest form of philosophy and intellectual attainment). This leads to the obvious conclusion that humans do not *understand* natural language very well. Natural language processing via purely formal procedures has been a dismal failure. The lack of understanding of natural languages is also empirically demonstrable. Confusion about the meaning of a person's words, intentions etc can be seen in every interaction with your boss/students/teachers/spouse/parents/kids etc etc. "You only think you understand what I said..." Jeffrey M. Jacobs CONSART Systems Inc. Technical and Managerial Consultants P.O. Box 3016, Manhattan Beach, CA 90266 (213)376-3802 CIS:75076,2603 BIX:jeffjacobs USENET: well!jjacobs "It used to be considered a hoax if there *was* a man in the box..." ------------------------------ Date: 13 Oct 86 22:07:54 GMT From: ladkin@kestrel.arpa Subject: Re: Searle, AI, NLP, understanding, ducks In article <1919@well.UUCP>, jjacobs@well.UUCP (Jeffrey Jacobs) writes: > Mathematics is very well understood, and > consists almost entirely of "formal procedures". I infer from your comment that you're not a mathematician. As a practicing mathematician (amongst other things), I'd like to ask precisely what you mean by *well understood*? And I would like to strongly disagree with your comment that doing mathematics consists almost entirely of formal procedures. Are you aware that one of the biggest problems in formalising mathematics is trying to figure out what it is that mathematicians do to prove new theorems? Peter Ladkin ladkin@kestrel.arpa ------------------------------ Date: 13 Oct 86 17:13:35 GMT From: jade!entropy!cda@ucbvax.Berkeley.EDU Subject: Re: Searle, Turing, Symbols, Categories In article <167@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes: Subject: your paper about category induction and representation First of all, I'd like a preprint of the full paper. Judging by the abstract, I have two main criticisms. The first one is that I don't see your point at all about "categorical perception". You say that "differences between reds and differences between yellows look much smaller than equal-sized differences that cross the red/yellow boundary". But if they look much smaller, this means they're NOT "equal-sized"; the differences in wave-length may be the same, but the differences in COLOR are much smaller. Your whole theory is based on the assumption that perceptual qualities are something physical in the outside world (e.g., that colors ARE wave-lengths). But this is wrong. Perceptual qualities represent the form in which we perceive external objects, and they're determined both by external physical conditions and by the physical structure of our sensory apparatus; thus, colors are determined both by wave-lengths and by the physical structure of our visual system. So there's no apriori reason to expect that equal-sized differences in wave-length will lead to equal-sized differences in color, or to assume that deviations from this rule must be caused by internal representations of categories. And this seems to completely cut the grounds from under your theory. My second criticism is that, even if "categorical perception" really provided a base for a theory of categorization, it would be very limited; it would apply only to categories of perceptual qualities. I can't see how you'd apply your approach to a category such as "table", let alone "justice". Actually, there already exists a theory of categorization that is along similar lines to your approach, but integrated with a detailed theory of perception and not subject to the two criticisms above; that is the Objectivist theory of concepts. It was presented by Ayn Rand in her book "Introduction to Objectivist Epistemology", and by David Kelley in his paper "A Theory of Abstraction" in Cognition and Brain Theory vol. 7 pp. 329-57 (1984); this theory was integrated with a theory of perception, and applied to categories of perceptual qualities, and in particular to perception of colors and of phonemes, in the second part of David Kelley's book "The Evidence of the Senses". Eyal Mozes BITNET: eyal@wisdom CSNET and ARPA: eyal%wisdom.bitnet@wiscvm.ARPA UUCP: ...!ihnp4!talcott!WISDOM!eyal Physical address: Department of Applied Math. Weizmann Institute of Science Rehovot 76100 Israel ------------------------------ End of AIList Digest ******************** From csnet_gateway Fri Oct 17 02:14:26 1986 Date: Fri, 17 Oct 86 02:14:13 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #218 Status: R AIList Digest Thursday, 16 Oct 1986 Volume 4 : Issue 218 Today's Topics: Queries - Lisp Machine Discussion List & PROLOG Dialects for VAX/VMS, AI Tools - Bug in Turbo Prolog & Garbage Collection, Seminar - Learning Apprentice Systems (UMD), Conferences - Machine Vision & Society for Philosophy and Psychology ---------------------------------------------------------------------- Date: Wed 15 Oct 86 14:04:23-EDT From: Arun Subject: Lisp machine discussion list As AIList get's deluged again with lisp machine stuff, I guess it's time to ask again, "Is there enough demand for a seperate discussion list for lisp machines?". I asked last time this happened, and there wasn't much reaction from the world. There are discussion groups for the equipment from each of the major manufacturers (info-1100, info-ti-explorer, slug, sun-spots, apollo), and even for some of the flavors of lisp (Franz-friends, info-xlisp), but nothing for discussing the relative merits of the different implementations of lisp for workstations, harware qualities, maintenance, directions that users would like to see workstations evolve towards, what things one likes/hates in lisp programming environments, and so on. I'm willing to work on starting up a mailing list and administer it if there is a large enough demand. Obviously, this is an inappropriate discussion for AIList. ...arun Arun Welch Lab for AI Research, Ohio State University. {ihnp4,cbosgd}!osu-eddie!welch welch@ohio-state.{CSNET,ARPA} welch@red.rutgers.edu (a guest account, but mail gets to me eventually) ------------------------------ Date: Wed, 15 Oct 86 09:35 N From: DEGROOT%HWALHW5.BITNET@WISCVM.WISC.EDU Subject: PROLOG-dialects-info wanted for VAX/VMS WANTED: Information about dialects of PROLOG-implementations for VAX/VMS, public-domain or commercial available. Send any pointers, references and the like to: Kees de Groot (DEGROOT@HWALHW5.BITNET) Tel. +31-8370- .KeesdeGroot (DEGROOT@HWALHW5.BITNET) o\/o THERE AINT NO (8)3557/ Agricultural University, Computer-centre [] SUCH THING AS 4030 Wageningen, the Netherlands .==. A FREE LUNCH! DISCLAIMER: My opinions are my own alone and do not represent any official position of my employer. ------------------------------ Date: Wed, 15 Oct 86 15:19:44 EDT From: David_West%UB-MTS%UMich-MTS.Mailnet@MIT-MULTICS.ARPA Subject: Bug in Turbo Prolog Most criticisms of Turbo Prolog have been only flames, but the following is, I think, an actual bug. If member is defined by: member(H,[H|_]):-!. member(H,[_|T]):-member(H,T). the goal: member([1,X],[[3,4],[1,2]]). will succeed (binding X to 2) or FAIL if the domain of the lowest level list elements is declared as integer or reference integer, respectively. It might be argued that this choice (whether or not to specify reference) is the user's responsibility, as in Algol-like languages; My view is that reference declarations are (like register declarations in C) "advice to the compiler", which should not alter the semantics of the program . This seems reasonable because: 1) the Turbo Prolog compiler will on its own initiative retype domains from value to reference, so it can't consider the distinction to affect the semantics; and 2) the abovementioned goal fails ONLY if the cut is present in the first clause of member; without this cut, Turbo Prolog (with or without reference specified) gives the same result as do other Prologs (for which, as expected, the presence or absence of the cut does not affect the result). ------------------------------ Date: Tue 14 Oct 86 20:02:05-EDT From: Arun Subject: Re: Garbage Collection >From: garren@STONY-BROOK.SCRC.SYMBOLICS.COM (Scott Garren) >Date: 6 Oct 86 14:05:00 GMT > >Relative to discussions of garbage collectors I would like to point out >that there are issues of scale involved. Many techniques that work >admirably on an address space limited to 8 Mbytes (Xerox hardware) >do not scale at all well to systems that support up to 1 Gbytes >(Symbolics). To pick a nit here, the Xerox machines are capable of addressing up to 32Mb. Arun Welch {ihnp4,cbosgd}!osu-eddie!welch welch@ohio-state.{CSNET,ARPA} welch@red.rutgers.edu (a guest account, but mail gets to me eventually) ------------------------------ Date: Tue, 14 Oct 86 13:27:33 EDT From: SubbaRao Kambhampati Subject: Seminar - Learning Apprentice Systems (UMD) Title: Learning Apprentice Systems Speaker: Prof. Tom Mitchell, Carnegie-Mellon University Location: Rm. 2324 Dept of CS, U of MD, College Park Time: 4:00pm We consider a class of knowledge-based systems called Learning Apprentices: systems that provide interactive aid in solving some problem, and that automatically acquire new knowledge by observing the actions of their users. The talk focuses on a particular Learning Apprentice, called LEAP, which is presently being developed in the domain of digital circuit design. LEAP is able to infer rules that characterize how to implement classes of circuit functions, by analyzing circuit fragments contributed by its users. The organization of LEAP suggests how similar learning apprentices might be constructed in a variety of task domains. (Refreshments will be served at 3:30pm in Rm. 3316) ------------------------------ Date: Wed 15 Oct 86 10:52:16-PDT From: Sandy Pentland Subject: Conference - Machine Vision FINAL CALL FOR PAPERS: Optical Society Topical Meeting on MACHINE VISION March 18-20, 1987 Hyatt Lake Tahoe, Incline Village, Nevada Topics will include: 3-D vision algorithms, image understanding, object recognition, motion analysis, feature extraction, novel processing hardware, novel sensors, and VSLI applications. Also, skiing. Invited speakers include: Bob Bolles (SRI), Peter Burt (RCA), Rodger Tsai (IBM), Demetri Terzopolis (SPAR), Rodger Dewar (Perceptron), J. Lowrie (Martin Marietta), P. Tamura and K. Coppock (Westinghouse), C. Jacobus (ERIM). Program committe: Alex Pentland, Glenn Sincerbox (co-chairs), Keith Nishihara, Harlyn Baker, Chris Goad, Steven Case, Aaron Gara, Charles Jacobus, Timothy Strand, Richard Young. WHAT TO SUBMIT: 25 WORD abstract and separate 4 PAGE camera-ready summary on standard 8 1/2 x 11 paper. Summary must begin with paper title, authors name and address, and authors should submit the original and one copy of both the abstract and the summary. Send your paper to: Optical Society of America Machine Vision 1816 Jefferson Place, N.W. Washington, D.C. 20036 DEADLINE: Nov. 3, 1986 ------------------------------ Date: 11 Oct 86 04:55:29 GMT From: rutgers!princeton!mind!harnad@lll-crg.arpa (Stevan Harnad) Subject: Society for Philosophy & Psychology: CALL FOR PAPERS [Please post hard copy locally] SOCIETY FOR PHILOSOPHY AND PSYCHOLOGY Call for Papers for 1987 Annual Meeting University of California at SAN Diego, June 21 - 23 1987 The Society for Philosophy and Psychology is calling for contributed papers and symposium proposals for its 13th annual meeting in San Diego. The Society consists of psychologists, philosophers, and other cognitive scientists with common interests in the study of behavior, cognition, language, the nervous system, artificial intelligence, consciousness, and the foundations of psychology. Past participants in annual meetings have included: N. Chomsky, D. Dennett, J. Fodor, C. R. Gallistel, J. J. Gibson, S. J. Gould, R. L. Gregory, R. J. Herrnstein, D. Hofstadter, J. jaynes, G. A. Miller, H. Putnam, Z. Pylyshyn, W. V. Quine, R. Schank, W. Sellars and P. Teitelbaum. Contributed Papers are refereed and selected on the basis of quality and relevance to both psychologists and philosophers. Psychologists, neuroscientists, linguists, computer scientists and biologists are encouraged to report experimental, theoretical and clinical work that they judge to have philosophical significance. Contributed papers are for oral presentation and should not exceed a length of 30 minutes (about 12 double-spaced pages). The deadline for submision is 12 January, 1987. Please send three copies to the Program Chairman: Professor William Bechtel Society for Philosophy and Psychology Department of Philosophy Georgia State University Atlanta GA 30303-3083 Phone: (404) 658-2277 Symposium proposals should also be sent to the above address as soon as possible. Local Arrangements: Professor Patricia Kitcher, B-002, Department of Philosophy, University of California at San Diego, La Jolla CA 92093. Individuals interested in becoming members of the Society should send $15 membership dues ($5 for students) to Professor Kitcher at the above address. SPP Officers: President: Stevan Harnad (Behavioral & Brain Sciences) President-Elect: Alvin I. Goldman (U. Arizona) Secretary Treasurer: Patricia Kitcher (UCSD) Program Chairman: William Bechtel (U. Georgia) Executive Committee: Myles Brand (U. Arizona) R. S. Jackendoff (Brandeis) Daniel Dennett (Tufts) William Lycan (U. N. Carolina) Fred Dretske (U. Wisconsin) John Macnamara (McGill) Jerome A. Feldman (U. Rochester) Carolyn Ristau (Rockefeller) Janet Fodor (CUNY) Anne Treisman (UC, Berkeley) Alison Gopnik (U. Toronto) Robert Van Gulick (Syracuse U.) Charles C. Wood (Yale) ------------------------------ End of AIList Digest ******************** From csnet_gateway Fri Oct 17 02:14:46 1986 Date: Fri, 17 Oct 86 02:14:32 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #219 Status: R AIList Digest Thursday, 16 Oct 1986 Volume 4 : Issue 219 Today's Topics: Bibliographies - Report Sources & Leff Citation Definitions & Bibliography of AI Applications ---------------------------------------------------------------------- Date: WED, 20 apr 86 17:02:23 CDT From: leff%smu@csnet-relay Subject: Report Sources Computer and Information Science Department University of Oregon Eugene, OR 97403 Department of Information and Computer Science University of California, Irvine Irvine, CA 92717 Research Institute for Advanced Computer Science Mail Stop 230-5, Nasa/Ames Research Center Moffett Field, California 94035 Attention: Technical Librarian Library Committee Department of Computer Science University at Buffalo (SUNY) 226 Bell Hall Buffalo, NY 14260 Prices are U. S. / Other Countries. Department of Computer Sciences Technical Report Center Taylor Hall 2.124 The University of Texas at Austin Austin, Texas 78712-1188 CS.TECH@UTEXAS-20 Arizona State University Computer Science Department Engineering and Applied Sciences Tempe, Arizona 85287 Computer Science Department New Mexico Tech Socorro, NM 87801 Technical Reports Librarian Princeton University Department of Computer Science Princeton, NJ 08544 Computing Research Laboratory University of Michigan Room 2222 Electrical Engineering and Computer Science Building Ann Arbor, Michigan 48109 Department of Computer Science and Engineering Oregon Graduate Center 19600 N. W. von Neumann Drive Beaverton, Oregon 97006-1999 ------------------------------ Date: WED, 20 apr 86 17:02:23 CDT From: leff%smu@csnet-relay Subject: defs for ai.bib37C and ai.bib40C D BOOK50 International Symposium on Logic Programming\ %D 1984 D MAG61 Proceedings of the 1986 Symposium on Symbolic and\ Algebriaic Computation\ %D JUL 21-23 1986 D MAG60 SIGSAM Bulletin\ %V 19\ %N 3\ %D AUG 1985 D MAG62 Image and Vision Computing\ %V 4\ %N 2\ %D MAY 1986 D MAG63 International Journal of BioMedical Computing\ %V 19\ %N 1\ %D JUL 1986 D MAG64 The Computer Journal\ %V 29\ %N 3\ %D JUN 1986 D MAG65 International Journal of Man-Machine Studies\ %V 24\ %N 2\ %D FEB 1986 D MAG66 Pattern Recognition\ %V 19\ %N 3\ %D 1986 D MAG67 Robotersysteme\ %V 2\ %N 2\ %D 1986 D MAG68 Review of The Electrical Communications Laboratories\ %V 34\ %N 3\ %D MAY 1986 D MAG69 Siemens Forschungs-und Entwicklungsberichte\ %V 15\ %N 3\ %D 1986 D MAG70 Computers in Biology and Medicine\ %V 16\ %N 3\ %D 1986 D MAG71 Future Generations Computer Systems\ %V 2\ %N 1\ %D MAR 1986 D MAG72 Computers and Artificial Intelligence\ %V 4\ %N 6\ %D 1985 D MAG73 Computer Vision, Graphics, and Image Processing\ %V 32\ %N 1\ %D OCT 1985 D MAG74 Computer Vision, Graphics and Image Processing\ %V 32\ %N 2\ %D NOV 1985 D MAG75 Infor\ %V 23\ %N 4\ %D NOV 1985 D MAG76 Pattern Recognition Letters\ %V 3\ %N 5\ %D SEP 1985 D MAG77 Fuzzy Sets and Systems\ %V 17\ %N 2\ %D NOV 1985 D MAG78 Kybernetika\ %V 21\ %N 5\ %D 1985 D BOOK51 Functional Programming Languages and Computer Architecture\ %E J. P. Jouannaud\ %S Lecture Notes in Computer Science\ %V 201\ %I Springer-Verlag\ %C Berlin-Heidelberg-New York\ %D 1985 D BOOK52 Automata, Languages and Programming\ %S Lecture Notes in Computer Science\ %V 201\ %I Springer-Verlag\ %C Berlin-Heidelberg-New York\ %D 1985 D MAG79 Journal of Logic Programming\ %V 2\ %D 1985\ %N 4 D MAG80 Computer Vision, Graphics, and Image Processing\ %V 35\ %N 1\ %D JUL 1986 D MAG81 Pattern Recognition Letters\ %V 4\ %N 2\ %D APR 1986 D MAG82 International Journal of Man-Machine Studies\ %V 24\ %N 4\ %D APR 1986 D MAG83 Journal of Parallel and Distributed Computing\ %V 3\ %N 2\ %D JUN 1986 D MAG84 Cybernetics and Systems\ %V 17\ %N 1\ %D 1986 D BOOK53 Architectures and Algorithms For Digital Image Processing\ %S Proceedings of the Society of Photooptical Instrumentation Engineers\ %V 596\ %E M. J. B. Duff\ %E H. J. Siegel\ %E F. J. Corbett\ %D 1986 D MAG85 Journal of Logic Programming\ %V 3\ %D 1986\ %N 1 D BOOK54 Rewriting Techniques and Applications (Dijon 1985)\ %S Lecture Notes in Computer Science\ %V 202\ %I Springer-Verlag\ %C Berlin-Heidelberg-New York\ %D 1985 D BOOK55 Topics in the Theoretical Bases and Applicatigons of Computer Science\ %I Akad. Kiado\ %C Budapest\ %D 1986 D MAG86 Computer Vision, Graphics and Image Processing\ %V 35\ %N 23\ %D AUG 1986 D MAG87 Computers and Operations Research\ %V 13\ %N 2-3\ %D 1986 ------------------------------ Date: WED, 20 apr 86 17:02:23 CDT From: E1AR0002%SMUVM1.BITNET@WISCVM.WISC.EDU Subject: Bibliography of AI Applications In response to several queries on AI applications on Engineering appearing in this forum, I am providing bibliographies on the following areas: 1) AI applications to Electrical Engineering 2) AI applications to Mechanical and Structural Engineering 3) AI applications to other Engineering Aspects 4) AI applications to Optimization Other sources of information are the International Journal for Artificial Intelligence in Engineering including the news section thereof, the Proceedings of the 1986 AAAI Workshop on Knowledge Based Expert Systems for Engineering Design, the bibliography section of the first paper under "EE references". Most of these are NOT included in the bib series of AI materials coming from the same login and appearing from time to time in AIList. Ignore the %W code. EE references %A D. Sriram %T Knowledge-Based Expert Systems; Collected Papers %I Department of Civil Engineering, Carnegie Mellon University %C Pittsburgh, PA %K AIME AIEE AIOE %W 15V %A C. Ronald Green %A Sajjan G. Shiva %T PECOS-An Expert Hardware Synthesis System %J Proceedings of the Fifth International Workshop on Expert Systems and Applications %D 1985 %K AIEE %W 17R %A Hyung-Sik Park %A Waldo C. Kobat %T KnowPLACE: Knowledge-Based Placement of PCB's %J Proceedings of the Fifth International Workshop on Expert Systems and Applications %D 1985 %K AIEE %W 17S %A Viviane Jonckers %T Knowledge Based Selection and Coordination of Specialized Algorithms %J Proceedings of the Fifth International Workshop on Expert Systems and Applications %D 1985 %K AIEE %W 17U %A C. Delorme %A F. Roux %A L. Demians Archimbaud %A M. Giambiasi %A R. L'Bath %A S. Mac Gee %A R. Charroffin %T A Functional Partitioning Expert System for Test Sequence Generation %J Proceedings of the 22nd Design Automation Conference %D 1985 %K AIEE %W 20Mc %A John Granacki %A David Knapp %A Alice Parker %T The ADAM Advanced Automation System Overview, Planner and Natural Language Interface %J Proceedings of the 22nd Design Automation Conference %D 1985 %K AIEE %W 20N %A Gotaro Odawara %A Kazuhiko Iijima %A Kazutoshi Wakabayashi %T Knowledge-Based Placement Technique for Printed Wiring Boards %J Proceedings of the 22nd Design Automation Conference %D 1985 %K AIEE %W 20O %A M. Giambiasi %A B. Mc Gee %A R. Lbath %A L. Demains Archimbaud %A C. Delorme %A P. Roux %T An Adaptive and Evolutive Tool for Describing General Hierarchical Models, Based on Frames and Demons %J Proceedings of the 22nd Design Automation Conference %D 1985 %K AIEE %W 20P %A Kai-Hsiung Chang %A William G. Wee %T A Knowledge Based Planning System for Mechanical Assembly Using Robots %J Proceedings of the 22nd Design Automation Conference %D 1985 %K AIEE %W 20Q %A Roostam Joobbani %A Daniel P. Siewiorek %T Weaver: A Knowledge Based Routing Expert %J Proceedings of the 22nd Design Automation Conference %D 1985 %K AIEE %W 20R %A M. A. Breuer %A xi-an Zhu %T A Knowledge-Based System for Selecting a Test Methodology for a PLA %J Proceedings of the 22nd Design Automation Conference %D 1985 %K AIEE %W 20S %A Tariq Samad %A Stephen W. Director %T Towards a Natural Language Interface for CAD %J Proceedings of the 22nd Design Automation Conference %D 1985 %K AIEE %W 20T %A Hugo J. de Man %A I. Bolsens %A Erik Vanden Meersch %A Johan van CleynenBreugel %T DIALOG: An Expert Debugging System for MOSVLSI Design %J IEEE Transactions on Computer-Aided Design %V CAD-4 %N 3 %D JUL 1986 %K AIEE %W 20U %A D. A. Lowther %A C. M. Saldanha %A G. Choy %T The Application of Expert Systems to CAD in Electromagnetics %J IEEE Transactions on Magnetics %V MAG-21 %N 6 %D NOV 1985 %K AIEE %W 20V __________________________________________________________________________ Mechanical and Structural Engineering %A J. S. Bennett %T SACON: A Knowledge-based Consultant for Structural Analysis %R HPP-78-23 %I Computer Science Department, Stanford University %D SEP 1978 %K AIME %W 9L %A J. S. Bennett %A R. Englemore %T SACON: A Knowledge-based Consultant for Structural Analysis %J Proceedings of the Sixth IJCAI %P 47-49 %D 1979 %K AIME %W need %A R. Fjelheim %A P. Syversen %T An Expert System for SESAM-69 Program Selection %R Computas Report 83-6010 %C Norway %D 1983 %W rbm %K AIME %A L. A. Lopez %A S. L. Elam %A T. Christopherson %T SICAD: A Prototype Implementation System for CAD D BOOK7 Proceedings of the ASCE Third Conference on Computers in Civil Engineering %C San Deigo, California %D April 1984 %P 84-93 %K AIME %W 13XYZ %A R. J. Melosh %A V. Marcal %A L. Berke %T Structural Analysis Consultation using Artificial Intelligence %B Research in Computerized Strucutral Analysis and Synthesis %I NASA %C Washington, D. C. %D OCT 1978 %W TR13 %K AIME %A L. A. Lopez %A D. Rehak %T Computer-Aided Enginering: Problems and Prospects %R Civil Engineering System Laboratory Research Series 8 %I University of Illinois %D July 1981 %K AIME %W TR8 %A J. M. Rivlin %A M. B. Hsu %A P. V. Marcal %T Knowledge-based Consultant for Finite Element Analysis %R Technical Report AFWAL-TR-80-3069 %I Flight Dynamics Laboratory (FIBRA), Wright-Patterson Airforce %D May 1980 %W need %K AIME %A A. D. Radford %A P. Hung %A J. S. Gero %T New Rules of Thumb from Computer-Aided Structural Design:Acquiring Knowledge for Expert Systems %J Proceedings CADD-84 %C United Kingdom %D 1984 %W rbm %K AIME %A J. S. Gero %A A. D. Radford %T Knowledge Engineering in Computer Graphics %J First Australasian Conference on Computer Graphics %C Sydney, Australia %D Aug 31-Sep 2 1983 %K AIME %W rbm %P 140-143 %A D. Sriram %A M. L. Maher %A J. Bielak %A S. J. Fenves %T Expert Systems for Civil Engineering - A Survey %R Technical Report R-82-137 %I Department of Civil Engineering, Carnegie-Mellon University %D June 1982 %K AIME %W ua %A D. Sriram %A M. Maher %A S. Fenves %T Applications of Expert Systems in Structural Engineering %B Proceedings Conference on Artificial Intelligence %C Rochestor, MI %D APR 1983 %P 379-394 %K AIME %W need %A M. L. Maher %A D. Sriram %A S. J. Fenves %T Tools and Techniques for Knowledge Based Expert Systems for Engineering Desig n %B Advances in Engineering Software %D 1984 %K AIME %W 13L %A D. Rehak %A C. Howard %A D. Sriram %T Architecture of an Integrated Knowledge Based Environment for Structural Engi neering Applications %J IFIP WG5.2 conference on Knowledge Engineering in Computer-Aided Design %D SEP 1984 %C Budapest, Hungary %K AIME %W 13J %A D. Sriram %A M. Maher %A S. Fenves %T Knowledge-based Expert Systems in Structural Design %J NASA Conference on Advances in Structures and Dynamics %D OCT 1984 %K AIME %W 13K %A R. Reddy %A D. Sriram %A N. Tyle %A R. Baneres %A M. Rychener %A S. J. Fenves %T Knowledge-based Expert Systems for Engineering Applications %J Proceedings IEEE International Conference on Man, Systems and Cybernetics %D DEC 1983 - JAN 1984 %C India %K AIME %W 13I %A J. S. Bennett %A R. S. Engelmore %T SACON: A Knowledge Based Consultant for Structural Analysis %J Proceedings Sixth IJCAI %P 47-49 %D 1979 %W 12H %K AIME %A D. Sriram %T A Bibliography on Knowledge-Based Expert Systems in Engineering %J SIGART %P 32-40 %D JUL 1984 %W 12I %K AIME %A H. Yoshiura %A Kikuo Fujimura %A T. L. Kunii %T Top-Down Construction of 3-D Mechanical Object Shapes from Engineering Drawin gs %J COMPUTER %D December 1984 %P 32-40 %K AIME %W 14D %A D. C. Brown %A B. Chandrasekaran %T Expert Systems for a Class of Mechanical Design Activity %J IFIP WG5.2 Working Conference on Knowledge Engineering in Computer Aided Desi gn %C Budapest, Hungary %D SEP 1984 %W 14L %K AIME %A D. C. Brown %T Capturing Mechanical Design Knowledge %I Computer Science Department %C Worcester, Massachussetts %W 14M %K AIME %A J. S. Arora %A G. Baenziger %T Uses of Artificial Intelligence in Design Optimization %J Computer Methods in Mechanics and Engineering %V 54 %N 3 %D MAR 1986 %P 303-324 %K AIME OPT %A D. Sriram %T Knowledge-Based Expert Systems; Collected Papers %I Department of Civil Engineering, Carnegie Mellon University %C Pittsburgh, PA %K AIME AIEE AIOE %W 15V %A D. Sriram %A S. J. Fenves %T Destiny: A Knowledge-Based Approach to Integrated Structural Design %I Department of Civil Engineering, Carnegie Mellon University %C Pittsburgh, PA %K AIME %W 15W %A T. A. Nguyen %A W. A. Perkins %A T. J. Laffey %T Application of LES to Advanced Design Verification %I Lockheed Research and Development %K AIME %W 16P %A H. L. LI %A P. Papalambros %T A Production System for Use of Global Optimization Knowledge %J JOMTAD %V 107 %D JUN 1985 %P 277-284 %W 18H %K AIME OPT %A J. W. Hou %T Shape Optimization of Elastic Hollow Bars %J JOMTAD %V 107 %D MAR 1985 %P 100-105 %W 18I %K AIME SO %A Hitoshi Furuta %A King-Sun Tu %A James T. P. Yao %T Structural Engineering Applications of Expert Systems %J CAD %V 17 %N 9 %D NOV 1985 %P 410-419 %K AIME %W 19Mc %A Mary Lou Maher %T Hi-Rise and Beyond: Directions for Expert Sytems in Design %J CAD %V 17 %N 9 %D NOV 1985 %P 420-426 %K AIME %W 19N %A A. D. Radford %A J. S. Gero %T Towards Generative Expert Systems for Architectuaral Detailing %J CAD %V 17 %N 9 %D NOV 1985 %P 428-434 %K AIME %W 19O __________________________________________________________________________ Other Engineering Fields not included in the above %A D. Sriram %T Knowledge-Based Expert Systems; Collected Papers %I Department of Civil Engineering, Carnegie Mellon University %C Pittsburgh, PA %K AIME AIEE AIOE %W 15V %A Mihai Barbuceanu %T A Domain Independent Architecture for Design Problem Solving %J Proceedings of the Fifth International Workshop on Expert Systems and Applications %D 1985 %K AIOE %W 17E %A Mihai Barbuceanu %T An Object-Centered Framework for Expert Systems in Computer-Aided Design %B Knowledge Engineering in CAD %I North Holland %E S. Gero %D 1985 %P 223-253 %K AIOE %W 17F %A Roland Rehmart %A Kristian Sandohl %A Olaf Granstedt %T Knowledge Organization in an Expert Sysem for Spot-Welding Robot Configuration %J Proceedings of the Fifth International Workshop on Expert Systems and Applications %D 1985 %K AIOE %W 17V %A Toshinori Watanabe %A Yoshiaki Nagai %A Chizuko Yasunobu %A Koji Sasaki %A Toshiro Yamanaka %T An Expert System for Computer Room Facility Layouts %J Proceedings of the Fifth International Workshop on Expert Systems and Applications %D 1985 %K AIOE %W 17W %A Larry F. Huggins %A John R. Barrett %A Don D. Jones %T Expert Systems: Concepts and Opportunities %J Agricultural Engineering %D JAN/FEB 1986 %V 67 %N 1 %P 21-24 %K AIOE %A Fabian C. Hadipriono %A Hock-Siew Toh %T Approximate Reasoning Models for Consequences on Structural Component Due to Failure Events %K AIOE %W 19F %A Michael Al. Rosenman %A John S. Gero %T Design Codes as Expert Systems %J CAD %V 17 %N 9 %D NOV 1985 %P 399-409 %K AIOE %W 19G %A David C. Browne %T Failure Handling in a Design Expert System %J CAD %V 17 %N 9 %D NOV 1985 %P 436-441 %K AIOE %W 19R %A Michael J. Pazzani %T Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning %I The Aerospace Corporation %K AIOE %W 20B %A Donald E. Brown %A Chlesea C. White, III %T An Expert System Approach to Boiler Design %I Department of Systems, Engineering, University of Virginia %K AIOE %W 20C %A Ernest Davis %T Conflicting Requirements in Reasoning About Solid Objects %K AIOE %W 20D %A Daniel R. Rehak %A H. Craig Howard %T Interfacing Expert Systems with Design Databases in Integrated CAD Systems %J CAD %V 17 %N 9 %D NOV 1985 %P 443-454 %W 20E %K AIOE %A Pual A. Fishwick %T The Role of Process Abstraction in Simulation %I Department of Computer and Information Science, University of Pennsylvania %K AIOE %W 20F %A Mark Wynot %T Artificial Intelligence Provides Real-Time Control of DEC's Material Handling Process %J IE %D APR 1986 %P 34+ %K AIOE %W 20G %A Jeannette M. Wing %A Farhad Arbab %T Geometric Reasoning: A New Paradigm for Processing Geometric Information %I Department of Computer Science, Carnegie-Mellon University %K AIOE %W 20K %A T. J. Grant %T Lessons for OR from AI: A Scheduling Case Study %J J. Opl Res. Soc %V 37 %N 1 %P 41-57 %D 1986 %W 20L %K AIOE %A Richard S. Shirley %A David A. Fortin %T Developing an Expert System for Process Fault Detection and Analysis %J Intech %P 51-58 %D APR 1986 %K AIOE %W 20M __________________________________________________________________________ Applications of AI to Optimization %A Jasbir S. Arora %A G. Baenziger %T Uses of Artificial Intelligence in Design Optimization %J Computer Methods in Applied Mechanics and Engineering %V 54 %D 1986 %P 303-323 %K AI OPT %W 19C %A S. Azarm %A P. Papalambros %T A Case for a Knowledge-Based Active Set Strategy %J JOMTAD %D MAR 1984 %P 77-81 %V 106 %K OPT AI %W 19H %A Alice M. Agogino %A Ann S. Almgren %T Symbolic Computation in Computer-Aided Optimal Design %I Department of Mechanical Engineering, University of California, Berkeley %D JUL 10, 1986 %K OPT AI %W 20I ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:09:09 1986 Date: Mon, 20 Oct 86 03:08:57 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #220 Status: R AIList Digest Friday, 17 Oct 1986 Volume 4 : Issue 220 Today's Topics: Bibliography - Leff Bibliography Continuation #1 ---------------------------------------------------------------------- Date: WED, 20 apr 86 17:02:23 CDT From: leff%smu@csnet-relay Subject: Bibliography (continued) %A V. F. Shangin %T Industrial Robots for Miniature Parts %I Mashinostroenie %C Moscow %D 1985 %K AT15 AI07 AA26 %A E. P. Popov %A E. I. Yurevich %T Robotics Engineering %I Mashinostroenie %C Leningrad %D 1984 %K AT15 AI07 AA26 %A L. S. Yampolskii %T Industrial Robotics %I Tekhnika %C Kiev %D 1984 %K AT15 AI07 AA26 %A Thaddeus J. Kowalski %T An Artificial Intelligence Approach to VLSI Design %I Kluwer Academic Publishers %C Norwell, MA %D 1985 %K AA04 AT15 %X 238 pages $34.95 ISBN 0-89838-169-X %A Rostam Joobbani %T An Artificial Intelligence Approach to VLSI Routing %I Kluwer Academic Publishers %C Norwell, MA %D 1985 %K AA04 AT15 %X 192 pages ISBN 0-89838-205-x $34.50 %A Narinder Pal Singh %T An Artificial Intelligence Approach to Test Generation %I Kluwer Academic Publishers %C Norwell, MA %K AA04 AT15 %X forthcoming, price and availability to be announced %A Anita Tailor %A Alan Cross %A David Hogg %A David Mason %T Knowledge Based interpretation of Remotely Sensed Images %J MAG62 %P 67-83 %K AI01 AI06 %A Tom Henderson %A Ashok Samal %T Multiconstraint Shape Analysis %J MAG62 %P 84-96 %K AI01 AI06 %A Robert Schalkoff %T Automated Reasoning About Image Motion Using A Rule-based Deduction System %J MAG62 %P 97-107 %K AI01 AI06 %A B. C. Vemun %A A. Mitiche %A J. K. Aggarwal %T Curvature-based Representation of Objects from Range Data %J MAG62 %P 107-115 %K AI01 AI06 %A J. A. D. W. Anderson %A K. D. Baker %A G. D. Sullivan %T 'Model': A POPLOG Package to Support Model-based Vision' %J MAG62 %P 115 %K AI01 AI06 T02 %A J. G. Llaurado %T Adapting Robotics More and More to Biology %J MAG63 %P 3-8 %K AI07 AA10 %A S. R. Ray %A W. D. Lee %A C. D. Morgan %A W. Airth-Kindree %T Computer Sleep Stage Scoring - An Expert System Approach %J MAG63 %P 43-61 %K AI01 AA10 AA11 %A I. D. Craig %T The Ariadne-1 Blackboard System %J MAG64 %P 235-240 %K AI01 %A G. Papakonstantinou %A J. Kontos %T Knowledge Representation with Attribute Grammars %J MAG64 %P 241-246 %K AI16 %A M. H. Williams %A G. Chen %T Translating Pascal for Execution on a Prolog-based System %J MAG64 %P 246-252 %K AI16 %A G. W. Smith %A J. B. H. du Boulay %T The Generation of Cryptic Crossword Clues %J MAG64 %P 282 %K AA17 %A Y. Ishida %A N. Adachi %A H. Tokumaru %T An Analysis of Self-Diagnosis Model by Conditional Fault Set %J International Journal of Computer and Information Sciences %V 14 %N 5 %D OCT 1985 %P 243-260 %A B. R. Gaines %A M. L. G. Shaws %T Foundations of Dialog Engineering: The Development of Human-Computer INteraction. Part II %J MAG65 %P 101-124 %K AI02 AA15 %A P. Shoval %T Comparison of Decision Support Strategies in Expert Consultation Systems %J MAG65 %P 125-140 %K AI01 AI13 %A S. Gottwald %A W. Pedrycz %T On the Suitability of Fuzzy Models: An Evaluation Through Fuzzy Integrals %J MAG65 %P 141-152 %K O04 %A A. L. Kamouri %A J. Kamouri %A K. H. Smith %T Training by Exploration: Facilitating the Transfer of Procedural Knowledge Through Analogical Reasoning %J MAG65 %P 171 %K AI04 AI16 %A C. Vallet %A J. Chastang %A J. D. Huet %T Partial Self-Reference in Models of Natural Systems and Spatiotemporal Reference Insufficiency of Physicians (French) %J Cybernetica %V 29 %N 2 %D 1986 %P 145-160 %K AA01 AI08 %A Y. F. Wang %A J. K. Aggarwal %T Surface Reconstruction and Representation of 3-D Scenes %J MAG66 %P 197-208 %K AI06 %A A. C. Bovik %A t. S. Huang %A D. C. Munson, Jr. %T Nonparametric tests for Edge Detection in Noise %J MAG66 %P 209-220 %K AI06 %A J. Katajainen %A O. Nevalainen %T Computing Relative Neighborhood Graphs in the Plane %J MAG66 %P 221-228 %K AI06 %A X. Li %A R. C. Dubes %T Tree Classifier Design with a Permuation Statistic %J MAG66 %P 229-236 %K AI06 %A M. Yamashita %A T. Ibaraki %T Distances Defined by Neighborhood Sequences %J MAG66 %P 237 %K AI06 %A Robert Michaelsen %A Donald Michie %T Prudent Expert Systems Applications can Provide a Competititve Weopon %J Data Management %V 24 %N 7 %D JUL 1986 %P 30-35 %K AI01 AA06 %A J. W. Park %T An Efficient Memory System for Image Processing %J IEEE Transactions on Computers %V 35 %N 37 %D JUL 1986 %P 669 %K AI06 %A Minoru Abe %A Makoto Kaneko %A Kazuo Tanie %T Study on Hexapod Walking Machine using an Approximate Straight-line Mechanism %J Journal of Mechanical Engineering Laboratory %V 40 %N 3 %D MAY 1986 %P 111-124 %K AI07 GA01 %A Tatsuo Arai %A Eiji Nakano %A Tomoaki Yano %A Ryoichi Hashimoto %T Hybrid Control System for a Manipulator and its Application %J Journal of Mechanical Engineering Laboratory %V 40 %N 3 %D MAY 1986 %P 133 %K AI07 GA01 %A H. Heiss %T Fundamentals About the Transformation of Coordinates for Robots (German) %J MAG67 %P 65-72 %K AI07 %X German %A P. Rojek %A J. Olomski %T Fast Coordinate Transformation and Processing of Command Signals for Continuous Path Robot Control %J MAG67 %P 73-82 %K AI07 %X (German) %A T. Tsumura %T Recent Developments of Automated Guided Vehicles in Japan %J MAG67 %P 91-98 %K GA01 AI07 %X (german) %A G. W. Kohler %T Mechanical Master-Slave Manipulators %J MAG67 %P 99-104 %K AI07 %X (german) %A G. Pritschow %A K. H. Hurst %T Design of Industrial Robots with Modular Components %J MAG67 %P 105-110 %K AI07 %X (german) %A T. Friedmann %T Robots in the Automotive Industry %J MAG67 %P 111-119 %K AI07 AA26 %X (german) %A R. Backmann %T Optoelectronic Sensors Sensoroptics - Some Basic Considerations for the Selection of Optical Sensor Components for Textile Identification %J MAG67 %P 120 %K AI06 %X (german) %A N. Sugamura %A S. Furui %T Speaker-Dependent Large Vocabulary Word Recognition Using the SPLIT Method %J MAG68 %P 327-334 %K AI05 GA01 %A K. Aikawa %A K. Shikano %T Spoken Word Recognition Using Spectrum and Power %J MAG68 %P 343-348 %K AI05 GA01 %A M. Sugiyama %A K. Shikano %T Unsupervised Learning Algorithm for Vowel Templates Based on Minimum Quantization Distortion %J MAG68 %P 357-362 %K AI05 GA01 %A E. Berti %T Forms of Rationality and the Future of Human Intelligence in the New Technological Age %J L'Elettrotcnica %V 73 %N 4 %D APR 1986 %K AI08 O05 %X (in itialian) %A R. Muller %A G. Horner %T Chemosensors with Pattern Recognition %J MAG69 %P 95-100 %K AI06 %A H. Fritz %A P. Wurll %T Tactile Force-torque Sensor for Performing Control Tasks in Robotics %J MAG69 %P 120-125 %K AI07 %A K. C. O'Kane %T An Expert Systems Facility for Mumps %J MAG70 %P 205-214 %K AA01 AI01 T03 %X [Mumps is an integrated language/database system often used in the medical records field. I heard Dr O'Kane speak on this work and he believed that such a system would allow expert systems to share clinical data with existing MIS systems in hospitals and make their introduction more practicable. - LEFF] %A H. Mansour %A M. E. Molitch %T A New Strategy for Clinical Decision Making: Censors and Neuroendocrinological Diseases %J MAG70 %P 215-222 %K AA01 AI01 %A Harold Stone %A Paolo Sipala %T Average Complexity of Depth-first Search with Backtracking and Cutoff %J IBM J. Res. and Dev. %V 30 %N 3 %D MAY 1986 %P 242-258 %K AI03 %T Artificial Intelligence - Wise Guys Wire Ships %J Marine Engineering Log %V 91 %N 6 %D JUN 1986 %P 119-122 %K AA18 AA05 %A Robert Cartwright %T A Practical Formal Semantic Definition and Verification System for TYPED LISP %D 1975 %I Garland Publishing %C New York, New York %K AT15 T01 AA08 %X Distinguished Dissertation System ISBN 0-8240-4420-7 %A Cordell Green %T The Application of Theorem Proving to Question-Answering Systems %D 1969 %I Garland Publishing %C New York, New York %K AT15 AI11 AA08 %X Distinguished Dissertation System ISBN 0-8240-4415-0 $18.00 %A James Richard Meehan %T The Metanovel: Writing Stories by Computer %D 1976 %I Garland Publishing %C New York, New York %K AT15 AI02 %X Distinguished Dissertation System ISBN 0-8240-4409-6 $18.00 %A Robert C. Moore %T Reasoning from Incomplete Knowledge in a Procedural Deduction System %D 1975 %I Garland Publishing %C New York, New York %K AT15 AI09 planner %X Distinguished Dissertation System ISBN 0-8240-4403-7 $13.00 %A Susan Speer Owicki %T Axiomatic Proof Techiques for Parallel Programs %D 1975 %I Garland Publishing %C New York, New York %K AT15 AA08 AI11 %X Distinguished Dissertation System ISBN 0-8240-4413-4 $20.00 %A Norihisa Suzuki %T Automatic Verification of Programs with Complex Data Structures %D 1976 %I Garland Publishing %C New York, New York %K AT15 AA08 AI11 %X Distinguished Dissertation System ISBN 0-8240-4425-8 $19.00 %A Robert Wilensky %T Understanding Goal-Based Stories %D 1978 %I Garland Publishing %C New York, New York %K AT15 AI02 PAM %X Distinguished Dissertation System ISBN 0-8240-4410-X $31.00 %A William A. Woods %T Semantics For a Question-Answering System %D 1967 %I Garland Publishing %C New York, New York %K AT15 AI02 %X Distinguished Dissertation System ISBN 0-8240-4405-3 $28.00 %A Stephen J. Andriole %T Applications in Artificial Intelligence %I Petrocelli Books %C Princeton, NJ %K AT15 AI07 AI02 AI01 AA18 %X $49.95 %A T. Gergely %T Cuttable Formulas for Logic Programming %J BOOK50 %P 299-310 %K AI10 %A Maria Virginia Aponte %A Jose Alberte Fernandez %A Philippe Roussel %T Editing First Order Proofs; Programmed Rules vs. Derived Rules %J BOOK50 %P 92-98 %K AI01 AI10 AI11 %A Hellfried Bottger %T Automatic Theorem Proving with Configurations %J Elektron. Informationsverarb. Kybernet. %V 21 %D 1985 %N 10-11 %P 523-546 %A G. Cedervall %T Robots for Definite Routine Analysis %J Kemisk Tidskrift %V 98 %N 4 %D APR 1986 %P 73-75 %K AI07 %X in Swedish %A Brian Harvey %T Computer Science Logo Style Volume 2: Projects, Styles and Techniques %I MIT Press %C Cambridge, Mass %D 1986 %K AT15 %A Daniel N. Osherson %A Michael Stob %A Scott Weinstein %T Systems that Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists %I MIT PRESS %C Cambridge, Mass %D 1986 %K AT15 AI04 AI08 %A K. H. Narjes %T Perspectives for European Cooperation %J MAG71 %P 13 %K GA03 %A M. Carpentier %T Community Strategy in Information Technology and Telecommunications %J MAG71 %P 19 %K AA08 %A M. Aigarain %T The Technological Perspective %J MAG71 %P 23 %K AI16 %A R. W. Wilmot %T The Market Perspective %J MAG71 %P 27 %K AT04 %A W. Dekker %T Issues Basic to the Development of a European Information Technology %J MAG71 %P 33 %K GA03 %A M. Gagao %T Cooperative R&D of Information Technologies Between the Government and Private Sector in Japan %J MAG71 %P 39 %K GA01 %A P. F. Smidt %T U. S. Industrial Cooperation in R&D %J MAG71 %P 45 %K GA03 %A J. M. Cadiou %T ESPRIT in Action %J MAG71 %P 51 %K GA03 %A F. F. Kuo %T A Return Visit to ICOT %J MAG71 %P 61 %K GA01 %T Network Support of Supercomputers: Conference Report. %J MAG71 %P 65 %K H04 %A W. J. Rapaport %T Philosophy, Artificial Intelligence, and the Chinese-Room Argument %J Abacus %V 3 %N 4 %D Summer 1986 %K AI16 %A D. I. Shapiro %T A Model for Decision Making under Fuzzy Conditions %J MAG72 %P 481 %K O04 AI13 AI08 %A G. Agre %T An Implementation of the Expert System DIGS for Diagnostics %J MAG72 %P 495 %K AA21 AI01 %A J. Hromkovic %T On One-Way Two-Headed Deterministic Finite State Automata %J MAG72 %P 503 %K AI16 %A E. Braunsteinerova %T Operating Alphabet Complexity of Homogenous Trellis Automata and Symmetric Functions %J MAG72 %P 527 %A I. Plander %T Projects of the New Generation Computer Systems and Informatics %J MAG72 %P 551 %A Julian Hewett %A Ron Sasson %T Expert Systems 1986, volume 1 --USA and Canada %I Ovum Limited %C London %K AT15 AI01 GA02 GA04 %A Philip Klahr %A Donald A. Waterman %T Expert Systems, Techniques, Tools and Applications %I Addison-Wesley %K AT15 Rand AI01 %A Michael Brady %A Jean Ponce %A Alan Yuille %A Haruo Asada %T Describing Surfaces %J MAG73 %P 1-28 %K AI06 %A Irving Biederman %T Human Image Image Understanding: Recent Research and a Theory %J MAG73 %P 29-73 %K AI06 AI08 AT08 AI16 %A Steven W. Zucker %T Early Orientation Selection: Tangent Fields and the Dimensionality of Support %J MAG73 %P 74-103 %K AI06 %A Martin D. Levine %A Ahmed M. Nazif %T Rule-Based Image Segmentation: A Dynamic Control Strategy Approach %J MAG73 %P 104-126 %K AI01 AI06 %A M. J. Magee %A J. K. Aggarwal %T Using Multisensory Images to Dervie the Structure of Three-Dimensional Object s - A Review %J MAG74 %P 145-157 %K AI06 AT08 %A Edgar A. Cohen %T Generalized Sloped Facet Models Useful in Multispectral Image Analysis %J MAG74 %P 171-190 %K AI06 %A A. Lashas %A R. Shurna %A A. Verikas %A A. Dosinas %T Optical Character Recognition Based on Analog Preprocessing and Automatic Fea ture Extraction %J MAG74 %P 191-207 %K AI06 %A John E. Wampler %T Enhancing Real-Time Perception of Quantum Limited Images from a Doubly Intensified SIT Camera System %J MAG74 %P 208-220 %K AI06 %A T. Y. Kong %A A. W. Roscoe %T A Theory of Binary Digital Pictures %J MAG74 %P 221-243 %K AI06 %A Ron Gershon %T Aspects of Perception and Computation in Color Vision %J MAG74 %P 244 %K AI06 %A I. G. Biba %T The adaptation of an Action-Planning System to Accomodate Problem Classes %J Cybernetics %V 21 %N 2 %D MAR-APR 1985 %P 242-253 %K AI09 %A Jaroslav Opatrny %T Parallel Programming Constructs for Divide-and-Conquer, and Branch and Bound Paradigms %J MAG75 %P 403-416 %K AI03 %A H. I. El-Zorkany %T Robot Programming %J MAG75 %P 430-446 %K AI07 %A David Butler %T Experience Using Artificial Intelligence %J Data Processing %V 27 %N 9 %D NOV 1985 %P 64 %K AI16 %A J. P. Keating %A R. L. Mason %T Some Practical Aspects of Covariance Estimation %J MAG76 %P 295-294 %K AI06 %A M. Krivanek %T An Application of Limited Branching in Clustering %J MAG76 %P 299-302 %K O06 %A W. Pedrycz %T Classification in a Fuzzy Environment %J MAG76 %P 303-308 %K O04 %A T. Kohonen %T Median Strings %J MAG76 %P 309-314 %K O06 %A W. G. Korpatsch %T A Pyramid that Grows by Powers of Two %J MAG76 %P 315-322 %K AI06 H03 %A C. Ronse %T A Simple Proof of Rosenfeld's Characterization of Digital Straight Line Segme nts %J MAG76 %P 323-326 %K AI06 %A I. K. Sethi %T A Genral Scheme for Discontinuity Detection %J MAG76 %P 327-334 %K AI06 %A O. Skliar %A M. H. Loew %T A New Method for Chracterization of Shape %J MAG76 %K AI06 %A F. C. A. Groen %A A. C. Sanderson %A J. F. Schlag %T Symbol Recognition in Electrical Diagrams Using Probabilistic Graph Matching %J MAG76 %K AI06 O06 AA04 %A Z. Pinjo %A D. Cyganski %A J. A. Orr %T Determination of 3-D Object Orientation From Projections %J MAG76 %K AI06 %A A. Bookstein %A K. K. Ng %T A Parametric Fuzzy Set Prediction Model %J MAG77 %P 131-142 %K O04 %A W. L. Chen %A R. J. Guo %A L. S. Shang %A T. Ji %T Fuzzy Match and Floating Threshold Strategy for Expert System in Traditional Chinese Medicine %J MAG77 %P 143-152 %K O04 AI01 AA01 %A D. G. Schwartz %T The Case for an Interval-based Representation of Linguistic Truth %J MAG77 %P 153-166 %K O04 AI02 %A L. O. Holl %A A. Kandel %T Studies in Possibilistic Recognition %J MAG77 %P 153-166 %K O04 AI06 %A M. Togai %T A Fuzzy Inverse Relation Based on Godelian Logic and its Applications %J MAG77 %P 211 %K O04 %A B. F. Buxton %A H. Buxton %A D. W. Murray %A N. S. Williams %T Machine Perception of Visual Motion %J GEC Journal of Research %V 3 %N 3 %D 1985 %P 145-161 %K AI06 %A M. S. Wilson %T An Evaluation of Manoeuvre Detector Algorithms %J GEC JOurnal of Research %V 3 %N 3 %D 1985 %P 181-190 %K AI06 AA18 %A J. M. Schurick %A B. H. Williges %A J. F. Maynard %T User Feedback Requirements with Automatic Speech Recognition %J Ergonomics %V 28 %N 11 %D NOV 1985 %K AI05 O01 %A R. Beg %T Image-Processing System Serves a Variety of Buses %J Computer Design %V 24 %N 16 %D NOV 15, 1985 %P 99 %K AI06 AT02 %A J. Zlatuska %T Normal Forms in the Typed Lambda-Calculus with Tuple Types %J MAG78 %P 366=381 %K T01 %A Osamu Furukawa %A Syohei Ishizu %T An Expert System for Adaptive Quality Control %J International Journal of General Systems %P 183-200 %V 11 %N 3 %D 1985 %K AA05 AI01 %A P. Carnevali %A L. Coletti %A S. Patarnello %T Image Processing by Simulated Annealing %J IBM Journal of Research and Development %V 29 %N 6 %P 569-579 %D NOV 1985 %K AI06 AI03 %A Michael Cross %T Down on th Automatic Farm %J New Scientist %V 108 %N 1483 %D NOV 21 1985 %P 56 %K AI07 AA23 %A S. Abramsky %A R. sykes %T Secd-m- a Virtual Machine for Applicative Programming %B BOOK51 %P 81-98 %A C. L. Hankin %A P.E. Osmon %A M. J. Shute %T COBWEB- A Combinator Reduction Architecture %B BOOK51 %P 99-112 %A P. Wadler %T How to Replace Failure by a List of Successes - A Method for Exception Handling, Backtracking, and Pattern Matching in Lazy Functional Languages %B BOOK51 %P 113-128 %K AI03 AI10 %A J. Hughes %T Lazy Memo-Functions %B BOOK51 %P 129-146 %A T. Johnsson %T Lambda-Lifting -0 Transforming Programs to Recursive Equations %B BOOK51 %P 190-203 %A S. K. Debray %T Optimizing Almost-Tail-Recursive Prolog Programs %B BOOK51 %P 204-219 %K T02 %A D. Patel %A M. Schlag %A M. Ercegovac %T vFP - an Environment for the Multi-Level specification, Analysis and Synthesis of Hardware Algorithms %B BOOK51 %P 238-255 %K AA08 AA04 %A J. Hughes %T A Distributed Garbage Collection Algorithm %B BOOK51 %P 256-272 %K T01 H03 %A D. R. Brownbridge %T Cyclic Reference Counting for Combinator Machines %B BOOK51 %P 273-288 %K T01 H03 %A D. S. Wise %T Design for a Multiprocessing Heap with On-Board Reference Couting %B BOOK51 %P 289-304 %K T01 H03 %A P. Dybjer %T Program Verification in a Logical Theory of Constructions %B BOOK51 %P 334-349 %K AA08 %A L. Augustsson %T Compiling Pattern Matching %B BOOK51 %P 368-381 %K O06 %A Mark Jerrum %T Random Generation of Combinatorial Structures for a Uniform Distribution (extended abstract) %B BOOK52 %P 290-299 %K O06 %A D. Kapur %A P. Narendran %A G. Sivakumar %T A Path Reordering for Proving Termination of Term Rewriting Systems %B BOOK47 %P 173-187 %K AI14 %A Deepak Kapur %A Mandayam Srivas %T A Rewrite Rule Based Approach for Synthesizing Abstract Data Types %B BOOK47 %P 188-207 %K AA14 AA08 %A Valentinas Kriauciukas %T A Tree-matching Algorithm %J Mat. Logika Primenen No. 1 %D 1981 %P 21-32 %K O06 %X Russian. English and Lithuanian Summaries %A Aida Pliuskeviciene %T Specification of cut-type Rules in Programming Logics with Recursion %J Mat. Logika Primenen No. 1 %D 1981 %P 33-60 %K AI10 %A C. Choppy %T A LISP Compiler for FP Language and its Proof via Algebraic Semantics %B BOOK47 %P 403-415 %K T01 AA08 %A Michael J. Corinthios %T 3D Cellular Arrays for Parallel/Cascade Image/Signal Processing %B Spectral Techniques and Fault Detection %P 217-298 %S Notes Rep. Comput. Sci. Appl. Math %V 11 %I Academic Press %C Orlando, Fla %D 1985 %K AI06 H03 %A D. O. Avetisyan %T The Probabilistic Approach to Construction of Intelligent Systems %J Mat. Voprosy Kibernet. Vychisl. Tekhn NO. 13 %D 1984 %P 5-21 %V 13 %K AI16 %X (Russian with Armenian Summary) %A Robert S. Boyer %A J. Strother Moore %T A Mechanical Proof of the Unsolvability of the Halting Problem %J JACM %V 31 %D 1984 %N 3 %P 441-458 %K AI11 %A N. A. Chuzhanova %T Grammatical Method of Synthesis of Programs %J Vychisl. Sistemy No. 102 %D 1984 %P 32-42 %N 102 %K AA08 %X russian %A S. M. Efimova %T Pi-Graphs for Knowledge Representation %I Akad. Nauk SSSR, Vychisl. Tsentr, Moscow %D 1985 %K AI16 %X Russian %A Melvin Fitting %T A Kripke-Kleene Semantics for Logic Programs %J MAG79 %P 295-312 %K AI10 %A D. M. gabbay %T N-Prolog: an Extension of PROLOG with Hypothetical Implication II. Logical Foundations, and Negation as Failure %J MAG79 %P 251-283 %K T02 %A Le Van Tu %T Negation-as-failure rule for General Logic Programs with Equality %J MAG79 %P 285-294 %K AI10 %A Zohar Manna %A Richard Waldinger %T Special Relations in Automated Deduction %B BOOK52 %P 413-423 %K AI11 %A Jack Minker %A Donald Perlis %T Computing Protected Circumscription %J MAG79 %P 235-249 %K AI11 AI15 %A J. A. Makowsky %T Why Horn Formulas Matter in Computer Science: Initial Structures and Generic Examples (extended abstract) %B BOOK47 %P 188-207 %K AI10 %A Xu Hua Liu %T The Input Semicancellation Resolution Principle on Horn Sets %J Kexue Tongbao %V 30 %D 1985 %N 16 %P 1201-1202 %K AI10 AI11 %X in chinese ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:09:25 1986 Date: Mon, 20 Oct 86 03:09:13 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #221 Status: R AIList Digest Friday, 17 Oct 1986 Volume 4 : Issue 221 Today's Topics: Bibliography - Leff Bibliography Continuation #2 ---------------------------------------------------------------------- Date: WED, 20 apr 86 17:02:23 CDT From: leff%smu@csnet-relay Subject: Bibliography (continued) %A R. Neches %A P. Langley %A D. Klahr %T Learning, Development and Production Systems %I Department of Information and Computer Science, University of California, Irvine %D JAN 1986 %R 86-01 %K AI01 AI04 %X 46 pages ($4.00) %A R. P. Hall %T Understanding Analogical Reasoning: Computational Approaches %D MAY 1986 %I Department of Information and Computer Science, University of California, Irvine %R 86-11 %K AI04 AT09 %X 60 pages ($5.00) %A P. Langley %A J. G. Carbonell %T Language Acquisition and Machine Learning %D JUN 1986 %I Department of Information and Computer Science, University of California, Irvine %R 86-12 %K AI02 AI04 %X 41 pages $3.00 %A J. C. Schlimmer %T A Note on Correlational Measures %D MAY 1986 %I Department of Information and Computer Science, University of California, Irvine %R 86-13 %X determining the degree that two events are interrelated 14 pages $2.00 %A B. Nordhausen %T Conceptual Clustering Using Relational Information %D JUN 1986 %I Department of Information and Computer Science, University of California, Irvine %R 86-15 %K AI04 O06 %X 15 pages $2.00 %A Peter J. Denning %T Expert Systems %I Research Institute for Advanced Computer Science, NASA Ames Research Center %R 85.17 %K AI01 %A Peter J. Denning %T Will Machines Ever Think? %I Research Institute for Advanced Computer Science, NASA Ames Research Center %R 86.12 %K AI16 %A Ajay Rastogi %A Sargur N. Srihari %T Recognizing Textual Blocks in Document Images Using the Hough Transform %I Department of Computer Science State University of New York at Buffalo %R 86-01 %K AI06 AA14 %X 1.00 /1.50 %A Pudcode Swaminathan %A Sargur N. Srihari %T Document Image Binarization: Second Derivative Versus Adaptive Thresholding %I Department of Computer Science State University of New York at Buffalo %R 86-02 %K AI06 %X $1.00/ $1.50 %A William J. Rapaport %T Philosophy of Artificial Intelligence: A Course Outline %I Department of Computer Science State University of New York at Buffalo %R 86-03 %K AI16 AT18 %X $1.00/$1.50 %A Shoshana L. Hardt %A William J. Rapaport %T Recent and Current Ai Research in the Department of Computer Science, SUNY-Buffalo %I Department of Computer Science State University of New York at Buffalo %R 86-05 %K AT21 %X $1.00/$1.50 %A Kemal Eboioglu %T An Expert System for Harmonization of Chorales in the Style of J. S. Bach %I Department of Computer Science State University of New York at Buffalo %R 86-09 %K AA25 AI01 %X $3.00/$4.00 289 pages %A Stuart C. Shapiro %T Symmetric Relations, Intensional Individuals, and Variable Binding %I Department of Computer Science State University of New York at Buffalo %R 86-10 %K AI16 AI02 AI01 %X dealing with relations such as "are adjacent" and "are related" %A Sargur N. Srihari %A Jonathan J. Hull %A Paul W. Palumbo %A Ching-Huei Wang %T Automatic Address Block Locatino: Analysis of Images and Statistical Data %I Department of Computer Science State University of New York at Buffalo %R 86-11 %K AI06 %X finding the destination address on a letter, magazine or parcel for the post office 63 pages $1.00/$1.50 %A S. L. Hardt %A D. H. Macfadden %A M. Johnson %A T. Thomas %A S. Wroblewski %T The Dune Shell Manual: Version 1 %I Department of Computer Science State University of New York at Buffalo %R 86-12 %K AI01 AA11 AA18 T03 common sense %X DUNE is Diagnostic Understanding of Natural Events, a shell that has been applied to threat assessment, personality assessment and common sense reasoning $1.00/$1.50 %A Janyce M. Wiebe %A William J. Rapaport %T Representing de re and de dicto belief reports in discourse and narrative %I Department of Computer Science State University of New York at Buffalo %R 86-14 %K AI02 AI16 %X $1.00/$1.50 %A William J. Rapaport %A Stuart C. Shapiro %A Janyce M. Wiebe %T Quasi-Indicators, Knowledge Reports, and Discourse %I Department of Computer Science State University of New York at Buffalo %R 86-15 %K AI02 AI16 de re de dicto %X $1.00/$1.50 %A David E. Rumenhart %A James L. McClelland %T Parallel Distributed Processing: Explorations in the Microstructures of Cognition, %I Library of Computer Science %K AT15 AI04 AI03 AI08 %X Two volume set for $35.95. Volume I: Foundations Volume II: Psychological Models %A Christian Lengauer %T A View of Automated Proof Checking and Proving %R TR-86-16 %D JUN 1986 %I University of Texas at Austin, Department of Computer Sciences %K AI11 %X $1.50 %A Manuel V. Hermengildo %T An Abstract Machine Based Execution Model for Computer Architecture Design and Efficient Implementation of Logic Programs in Parallel %R TR-86-20 %D JUN 1986 %I University of Texas at Austin, Department of Computer Sciences %K AI10 H03 %X $5.00 %A Nicholas V. Findler %A Timothy W. Bickmore %A Robert F. Cromp %T A General-Purpose Man-Machine Environment to Aid in Decision Making and Planning with Special Reference to Air Traffic Control %I Arizona State University, Computer Science Department %R TR-84-001 %K AI13 AI09 O01 %A Nicholas V. Findler %A Timothy W. Bickmore %A Robert F. Cromp %T A General-Purpose Man-machine Environment with Special Reference to Air Traffic Control %I Arizona State University, Computer Science Department %R TR-84-002 %K AI13 AI09 O01 %A Nicholas V. Findler %A Ron Lo %T An Examination of Distributed Planning in the World of Air Traffic Control %I Arizona State University, Computer Science Department %R TR-84-004 %K AI13 AI09 O01 %A Ben Huey %T Using Register Transfer Languages for Knowledge-Based Automatic Test Generation %I Arizona State University, Computer Science Department %R TR-84-011 %K AA04 %A F. Golshani %T Tools for the Construction of Expert Database Systems %I Arizona State University, Computer Science Department %R TR-84-013 %K AA09 AI01 %A Ben M. Huey %T The Heuristic State Search Algorithm %I Arizona State University, Computer Science Department %R TR-84-018 %K AI03 %A F. Golshani %A A. Faustin %T The Eductive (sic) Knowledge Engine-Preliminary Investigations %I Arizona State University, Computer Science Department %R TR-84-023 %K AI16 %A A. L. Pai %A J. W. Pan %T A Computer Graphics Kinematic Simulation System for Robot Manipulators %I Arizona State University, Computer Science Department %R TR-85-003 %K AI07 %A Nicholas V. Findler %T Air Traffic Control, A Challenge for Artificial Intelligence %I Arizona State University, Computer Science Department %R TR-85-006 %K AI16 %A Richard L. Madarasz %A Loren C. Heiny %A Norm E. Berg %T The Design of an Autonomous Vehicle for the Handicapped %I Arizona State University, Computer Science Department %R TR-85-010 %K AI07 AA19 %A N. V. Findler %A P. Bhaskaran %A Ron Lo %T Two Theoretical Issues Concerning Expert Systems %I Arizona State University, Computer Science Department %R TR-85-012 %K AI01 %A Richard Madarasz %A Kathleen M. Mutch %A Loren C. Heiny %T A Low-Cost Binocular Imaging System for Research and Education %I Arizona State University, Computer Science Department %R TR-85-013 %K AI06 AT18 %A Robert F. Cromp %T The Task, Design and Approach of the Advice Taker/Inquirer System %I Arizona State University, Computer Science Department %R TR-85-014 %K AI16 %A Kathleen M. Mutch %T The Perception of Translation in Depth Using Stereoscopic Motion %I Arizona State University, Computer Science Department %R TR-85-015 %K AI06 %A Ron Lo %A Cher Lo %A N. V. Findler %T A Pattern Search Technique for the Optimization Module of a Morph-Fitting Package %I Arizona State University, Computer Science Department %R TR-86-001 %K AI03 %A N. V. Findler %A Laurie Igrif %T Analogical Reasoning by Intelligent Robots %I Arizona State University, Computer Science Department %R TR-86-003 %K AI07 AI16 %A Nicholas V. Findler %T The Past, Present and Future of Artificial Intelligence - A Personal View %I Arizona State University, Computer Science Department %R TR-86-004 %K AT14 %A Stephen Fickas %T Automating the Transformational Development of Software %R CIS-TR-85-01 %I Computer and Information Science Department, University of Oregon %C Eugene, OR %D 1985 %K AA08 %A John S. Conery %A Dennis F. Kibler %T AND Parallelism and Nondeterminism in Logic Programs %R CIS-TR-85-02 %I Computer and Information Science Department, University of Oregon %C Eugene, OR %D 1985 %K H03 AI10 %A Stephen Fickas %A David Novick %A Rob Reesor %T Building Control Strategies in a Rule-Based System %R CIS-TR-85-04 %I Computer and Information Science Department, University of Oregon %C Eugene, OR %D 1985 %K AI01 metaknowledge %A Stephen Fickas %A David Novick %T Control Knowledge in Expert Systems: Relaxing Restrictive Assumptions %R CIS-TR-85-05 %I Computer and Information Science Department, University of Oregon %C Eugene, OR %D 1985 %K AI01 metaknowledge %A Stephen Fickas %T Design Issues in a Rule-Based System %R CIS-TR-85-06 %I Computer and Information Science Department, University of Oregon %C Eugene, OR %D 1985 %K AI01 metaknowledge %A Kent A. Stevens %A Allen Brookes %T The Concave Cusp as a Determiner of Figure Ground %R CIS-TR-85-08 %I Computer and Information Science Department, University of Oregon %C Eugene, OR %D 1985 %K AI06 %X (of interest to researchers on texture perception ) %A Stephen Fickas %A David Novick %A Rob Reesor %T An Environment for Building Rule-Based Systems: An Overview %R CIS-TR-85-10 %I Computer and Information Science Department, University of Oregon %C Eugene, OR %D 1985 %K AI01 T03 %A Stephen Fickas %T A Knowledge-Based Approach to Specification Acquisition and Construction %R CIS-TR-85-13 %I Computer and Information Science Department, University of Oregon %C Eugene, OR %D 1985 %K AA08 %A Kazem Taghva %T Constructive Fully Abstract Models of Typed Lambda-Calculi %R CSR 159 %I Computer Science Department, New Mexico Tech %C Socorro, NM %D DEC 1983 %K AA08 %A Allan M. Stavely %T Inference From Models of Software Systems %R CSR 162 %I Computer Science Department, New Mexico Tech %C Socorro, NM %D MAY 1984 %K AA08 %A Raymond D. Gumb %A Sarah Bottomley %A Alex Trujillo %T Sandia National Laboratories SURP Grant 95-2931 Final Report: Filming a Terrain Under Uncertainty Using Temporal and Probabilistic Reasoning %R CSR 172 %I Computer Science Department, New Mexico Tech %C Socorro, NM %D AUG 1986 %K AI06 O04 %A Andrew W. Appel %T Garbage Collection Can Be Faster than Stack Allocation %R TR-045-86 %D JUN 1986 %I Princeton University, Department of Computer Science %K H02 T01 %A Richard J. Lipton %A Daniel Lopresti %A J. Douglas Welsh %T The Total DNA Homology Experiment %R TR-020-86 %I Princeton University, Department of Computer Science %K AA10 O06 %X plan to compare all known DNA sequences with each other to find homologies They will be using a systolic array for DNA sequence matching and hope to complete the project within one years time. %A Bernard Nadel %T Representation-Selection for Constraint Satisfaction Problems: A Case Study Using n-Queens %D MAR 1986 %R CRL-TR-5-86 %I University of Michigan, Computer Research Laboratory %K AI03 AA17 %A Bernard Nadel %T Theory-Based Search-Order Selection for Constraint Satisfaction Problems %D APR 1986 %R CRL-TR-6-86 %I University of Michigan, Computer Research Laboratory %K AI03 %A J. T. Park %A T. J. Teory %T Heuristics for Data Allocation in Local Area %D MAY 1986 %R CRL-TR-7-86 %I University of Michigan, Computer Research Laboratory %K AA09 %X describes heuristics for allocating data where update is done by broadcast %A K. Shin %A P. Ramanathan %T Diagnosis of Malicious Processors in a Distributed Computing System %D MAY 1986 %R CRL-TR-8-86 %I University of Michigan, Computer Research Laboratory %K AA21 %A Hary H. Porter, III %T Earley Deduction %R CS/E 86-002 %I Oregon Graduate Center %D 1986 %K T02 Datalog %A Clifford Walinsky %T Constructive Negation in Horn-Clause Programs %R CS/E 86-003 %I Oregon Graduate Center %D 1986 %K AI10 %A Dennis M. Volpano %T Translating an FP Dialect to L - A Proof of Correctness %R CS/E 85-001 %I Oregon Graduate Center %D 1985 %K AA08 %A Richard B. Kieburtz %T The G-Machine: A Fast, Graph-Reduction Evaluator %R CS/E 85-002 %I Oregon Graduate Center %D 1985 %A Richard B. Kieburtz %T Incremental Collection of Dynamic, List-Structured Memories %I Oregon Graduate Center %D 1985 %R CS/E 85-008 %K T01 H03 %X incremental garbage collection %A Ashoke Deb %T An Efficient Garbage Collector for Graph Machines %I Oregon Graduate Center %D 1984 %R CS/E 84-003 %K H03 %A John S. Givler %T Pattern Recognition in FP Programs %I Oregon Graduate Center %D 1983 %R CS/E 83-003 %K O06 AI06 ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:09:49 1986 Date: Mon, 20 Oct 86 03:09:34 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #222 Status: R AIList Digest Friday, 17 Oct 1986 Volume 4 : Issue 222 Today's Topics: Bibliography - Leff Bibliography Continuation #3 ---------------------------------------------------------------------- Date: WED, 20 apr 86 17:02:23 CDT From: leff%smu@csnet-relay Subject: Bibliography (continued) %A L. S. Fainzilberg %A G. A. Shklyar %T Estimation of Attribute Utility in Statistical Recognition of Two Classes %J Soviet J. Automat. Inform. Scie. %V 189 %N 5 %P 81-86 %K O04 O06 %A Peter Naur %T Thinking and Turing's Test %J BIT %V 26 %D 1986 %N 2 %P 175-187 %K AI16 %A Persi Diaconis %A Mehrdad Shahshani %T Products of Random Matrices and Computer Image Generation %B Random Matrices and Tehir Applications %P 173-182 %S Contemp. Math. %V 50 %I Amer. Math. Soc. %C Providence, R. I. %D 1986 %K AI06 %A V. A. Nepomnyaschii %T Problem-oriented Program Verification %J Programmirovanie 1986 %N 1 %P 3-13 %K AA08 %A Yong Qiang Sun %A Bao Xing Tang %T Strong Verification of Nested-loop Programs %J J. Shanghai Jiatong Univ. %D 1984 %N 6 %P 1-10 %K AA08 %X Chinese with English summary %A A. Browne %T Vision and the Robot %J Philips Journal of Research %V 41 %N 3 %D 1986 %P 232-246 %K AI06 AI07 %A B. J. Falkowski %A L. Schmitz %T A Note on the Queen's Problem %J Information Processing Letters %V 23 %N 1 %D JUL 20, 1986 %K AI03 AA17 %A Huia-Chuan Chen %A J. H. Fang %T A Heuristic Search Method for Optimal Zonation of Well Logs %J Mathematical Geology %V 18 %N 5 %D 1986 %P 489-500 %K AA03 AI06 AI03 %X improved the Houwkins and Merium algorithm by 7 to 50 fold %A A. Kong %A G. O. Barnett %A F. Mosteller %A C. Youtz %T How Medical Professional Evaluate Expressions of Probability %J New England Journal of Medicine %V 315 %D SEP 18, 1986 %N 12 %P 740-744 %K AA01 AI02 AI01 %X explains what is meant in probabilistic terms by doctors by such phrases as "likely" %A G. Vontrzebiatowski %A B. Bank %T On the Convergence of the Fuzzy Clustering Algorithm Fuzzy ISODATA %J Zeitschrift fur Angewandte Mathematik und Mechanik %P 201-208 %V 66 %N 6 %D 1986 %K O04 O06 %A Makoto Kaneko %A Minoru Abe %A Eiichi Horiuchi %A Kazuo Tanie %T Study on Hexapod Walking Machine using an Approximate Straight Line Mechanism Third Report; A Control Method for Proceeding over Soft Ground %J Journal of Mechanical Engineering Laboratory %V 40 %N 4 %D JUL 1986 %K AI07 %A A. G. Erdman %A T. Thompson %A D. R. Riley %T Type Selection of Robot and Gripper Kinematic Topology Using Expert Systems %J International Journal of Robot Research %V 5 %N 2 %D 1986 %P 183 %K AA05 AI01 AI07 %X [. There are many other articles on this issue on robot kinematics (this was a special issue). I do not include those in this bibliography.] %A James J. Clark %A Peter D. Lawrence %T A Theoretical Basis for Diffrequency Stereo %J MAG80 %P 1-19 %K AI06 %A Brian G. Schunck %T The Image Flow Constraint Equation %J MAG80 %P 20-46 %K AI06 %A Teresa M. Silberberg %A David A. Harwood %A Larry S. Davis %T Object Recognition Using Oriented Model Points %J MAG80 %P 47-71 %K AI06 %A Haluk Derin %A William S. Cole %T Segment of Textured Images Using Gibbs Random Fields %J MAG80 %P 72-98 %K AI06 %A Theo Pavlidis %T A Vectorizer and Feature Extractor for Document Recognition %J MAG80 %P 111 %K AI06 %A Matthew Hennessy %T Proving Systolic Systems Correct %J ACM Transactions on Programming Languages and Systems %V 8 %N 3 %D JUL 1986 %P 344-387 %K AA08 AA04 AI11 %A Krzysztof R. Apt %T Correctness Proofs of Distributed Termination Algorithms %J ACM Transactions on Programming Languages and Systems %V 8 %N 3 %D JUL 1986 %P 388-407 %K AA08 AI11 %A A. Pathak %A S. K. Pal %T A Generalized Learning Algorithm Based on Guard Zones %J MAG81 %P 63-70 %K AI04 %A S. Larsen %A L. N. Kanal %T Analysis of k-nearest Neighborhood Branch and Bound Rules %J MAG81 %P 71-78 %K AI03 %A F. Pasian %A C. Vuerli %T Core-line Tracing for Fuzzy Image Subsets %J MAG81 %P 93 %K O04 AI06 %A O. R. Polonskaya %T Logic-Semantic Connectors of the English Language as Formal Indicators of Text Coherence %J Nauchno-Tekhnicheskaya Informatsiya Seriya II - Informatsionnye Protessy I Systemy %N 6 %D 1986 %P 19-22 %K AI02 %A J. Victor %T Bell-Labs Models Parallel Processor on Neural Networks %J Mini-Micro Systems %V 19 %N 10 %D AUG 1986 %P 43+ %K AI12 H03 %A C. Dede %T A Review and Synthesis of Recent Research in Intelligent Computer-Assisted Instruction %J MAG82 %P 329-354 %K AA07 AT08 AT21 %A J. S. Greenstein %A L. Y. Arnaut %A M. E. Revesman %T An Empirical Comparison of Model-Based and Explicit Communication for Dynamic Human-Computer Task Allocation %J MAG82 %P 355-364 %K AI08 O01 %A C. G. Leedham %A A. C. Downton %T On-Line Recognition of Pitman Handwritten Shorthand %J MAG82 %P 375-394 %K AI06 %A P. N. Crowley %T The Use of Q-Analysis and Q-Factor Weightings to Derive Clinical Psychiatric Syndromes %J MAG82 %P 395-408 %K AA11 O04 %A Concettina Guerra %T A VLSI Algorithm for the Optimal Detection of a Curve %J MAG83 %P 206-214 %K AI06 %A Bruce K. Hillyer %A David Elliot Shaw %T Execution of OPS5 Production Systems on a Massively Parallel Machine %J MAG83 %P 236-268 %K H03 AI01 %A Salvatore J. Stolfo %A Daniel P. Miranker %T The DADO Production System Machine %J MAG83 %P 269 %K AI01 H03 %A Gerrit Broekstra %T Organizational Humanity and Architecture: Duality and Complementarity of PAPA -Logic and MAMA-Logic in Managerial Conceptualizations of Change %J MAG84 %P 13-42 %K AI08 AA11 AA06 %A Stuart A. Umpleby %T Self-Authorization: A Characteristic of Some Elements in Certain Self-Organiz ing Systems %J MAG84 %P 79-88 %K H03 AI12 %A R. M. Lougheed %A C. M. Swonger %T An Analysis of Computer Architectural Factors Contributing to Image Processor Capacity %B BOOK53 %P 3-13 %K AI06 %A O. R. Hinton %A H. G. Kim %T A Bit-Sequential VLSI Pixel-Kermel Processor for Image Processing %B BOOK53 %P 14-20 %K AI06 H03 %A D. J. Skellern %T A Very Large Scale Integration (VLSI) System for Image Reconstruction from Projections %B BOOK53 %P 21-26 %K AI06 %A P. W. Besslich %T Parallel Architecture for Line-Scanned Images %B BOOK53 %P 27-35 %K AI06 H03 %A R. P. W. Duin %A H. Haringa %A R. Zeelen %T A Hardware Design for Fast 2-D Percentile Filtering %B BOOK53 %P 36-40 %K AI06 %A R. Boekamp %A F. C. A. Groen %A F. A. Gerritsen %A R. J. Vanmunster %T Design and Implementation of a Cellular Logic VME Processor Module %B BOOK53 %P 41-45 %K AI06 %A J. L. Basille %A S. Castan %T Multilevel Architectures for Image Processing %B BOOK53 %P 46-53 %K AI06 H03 %A J. Rommelaere %A L. Vaneycken %A P. Wambacq %A A. Oosterlinck %T A Microprogrammable Processor Architecture for Image Processing %B BOOK53 %P 59-67 %K AI06 %A M. Suk %A S. S. Pyo %T A Geometry Processor for Image Processing and Pattern Recognition %B BOOK53 %P 68-73 %K AI06 %A P. W. Pachowicz %T Image Processing by a Local-SIMD Co-Processor %B BOOK53 %P 82-87 %K AI06 AI03 %A V. Cantoni %A L. Carrioli %A O. Catalano %A L. Cinque %A V. Digesu %A M. Ferretti %A G. Gerardi %A S. Levialdi %A R. Lombardi %A A. Machi %A R. Sterfanelli %T The Papia Image Analysis System %B BOOK53 %P 88-97 %K AI06 %A J. Ronsin %A D. Barba %A S. Raboisson %T Comparison Between Cooccurrence Matrices, Local Histograms and Curvilinear Integration for Texture Characterization %B BOOK53 %P 98-104 %K AI06 %A N. Lins %T Refinement of Spectral Methods for Use in Texture Analysis %B BOOK53 %P 105-111 %K AI06 %A M. Slimani %A C. Roux %A A. Hillioun %T Image Segmentation by Cluster Analysis of High Resolution Textured SPOT Image s %B BOOK53 %P 112-119 %K AI06 %A A. Beckers %A L. Dorst %A L. T. Young %T The Choice of Filter Parameters for non-Linear Grey-Value Image Processing %B BOOK53 %P 120-128 %K AI06 %A J. Illingworth %A J. Kittler %T A Parallel Threshold Selection Algorithm %B BOOK53 %P 129-134 %K AI06 H03 %A R. Samy %T An Adaptive Image Sequence Filtering Scheme Based on Motion Detection %B BOOK53 %P 135-144 %K AI06 %A B. K. Ghaffary %T A Review of Image Matching Techniques %B BOOK53 %P 164-172 %K AI06 %A K. Martinez %A D. E. Pearson %T PETAL A Parallel Processor for Real-Time Primitive Extraction %B BOOK53 %P 173-175 %K AI06 O03 %A T. J. Dennis %A L. J. Clark %T Real Time Detection of Spot-Type Defects %B BOOK53 %P 178-183 %K AI06 O03 %A E. Egeli %A F. Klein %A G. Maderlechner %T Model-Based Instantiation of Symbols from Structurally Related Image Primitives %B BOOK53 %P 184-189 %K AI06 %A R. L. Shoemaker %A P. H . Bartels %A H. Bartels %A W. G. Griswold %A D. Hillman %A R. Maenner %T Image-Data-Driven Dynamically-Reconfigurable Multiprocessor System in Automated Histopathology %B BOOK53 %P 190-198 %K AI06 AA10 %A T. Lorch %A J. Bille %A M. Frieben %A G. Stephan %T An Automated Biological Dosimetry System %B BOOK53 %P 199-206 %K AI06 AA10 %A C. Katsinis %A A. D. Poularikas %T Pattern Recognition of Zooplankton Images Using a Circular Sampling Technique %B BOOK53 %P 207-211 %K AI06 AA10 %A D. Lecomte %A J. Beullier %A D. Grangeon %T Image Porcessing Adapted to Radiographs %B BOOK53 %P 212-218 %K AI06 AA01 %A Zohar Manna %A Richard Waldinger %T The Logical Basis for Computer Programming. Vol I. Deductive Reasoning %I Addison-Wesley Publishing Co. %C Reading, MASS %D 1985 %K AA08 AI11 AT15 %A A. S. Morozov %T Logic with Incomplete Information as an Information System in the Sense of Scott %J Vychisl. Sistemy NO. 107 %D 1985 %P 71-79 %K AI16 %A B. C. Moszkowski %T Executing Temporal Logic Programs %I Cambridge University Press %C Cambridge-New York %D 1986 %K AT15 AI10 %X ISBN 0-521-31099-7 %A A. P. Sistla %A E. M. Clarke %A N. Francez %A A. R. Meyer %T Can Message Buffers be Axiomatized in Linear Temporal Logic %J Inform. and Control %V 63 %N 1-2 %P 88-112 %K AA08 AI11 %A Wolfgang Wechler %T R-fuzzy Computation %J J. Math. Anal. Appl. %V 115 %D 1986 %N 1 %P 225-232 %K O04 %A Luis Aguila Feros %A Jose Ruiz Shulcloper %T A Bm-Algorithm for Processing K-valent Data in Recognition Problems %J Cinc. Mat. (Havana) %V 5 %D 1984 %N 3 %P 89-101 %K AI16 %X Spanish. English Summary %A Vincent Digricoli %A Malcolm Harrison %T Equality-based Binary Resolution %J JACM %V 33 %D 1986 %N 2 %P 253-289 %K AI11 %A M. H. van Emden %T Quantitative Deduction and its Fix-Point Theory %J MAG85 %P 37-53 %K AI10 %A Hong Fan %A Jorge L. C. Sanz %T Comments on "Direct Fourier Reconstruction in Computer Tomography" [IEEE Trans. Acoust. Speech Signal Process 29 (1981) no. 2. 237-245 by H. Stark, J. W. Woods, I. Paul and R. Hingorani %J IEEE Trans. Acoust. Speech Signal Process. %V 33 %D 1985 %N 2 %P 446-449 %K AI06 AA01 AT13 %A D. M. Gabbay %A M. J. Sergot %T Negation as Inconsistency %J MAG85 %P 1-35 %K AI10 %A Han Rong Lu %T Some Problems in Logic Program Design %J Comput. Sci %D 1986 %N 1 %P 38-39 %K AI10 O02 %X (Chinese) %A Anca L. Ralescu %T A Note on Rule Representation in Expert Systems %J Inform. Sci %V 38 %D 1986 %N 2 %P 193-203 %K AI01 %A Yu A. Zuev %T Probabilistic Model of a Committee of Classifiers %J Zh. Vychisl. Mat. i Mat. Fiz %V 26 %D 1986 %N 2 %P 276-292 %K H03 O04 %X (russian) %A Irena Pevac %T Heuristic for Avoiding Skolemization in Theorem Proving %J Publ. Inst. Math. (Beograd) (N. S.) %V 38 %N 52 %D 1985 %P 207-213 %K AI11 %A A. A. Voronkov %T A Method of Search for a Proof %J Vychisl. Sistemy No. 107 %D 1985 %P 109-123 %K AI03 AI11 %X (Russian) %A Kiem Hoang %T Geometric Transforms of Digital Images %J Rostock. Math. Kolloq. No 28 %D 1985 %P 87-98 %K AI06 %A Jacques Loeckx %A Kurt Sieber %A Ryan D. Sansifer %T The Foundations of Program Verication %S Wiley-Teubner Series in Computer Science %I John Wiley and Sons %C Chichester %D 1984 %K AT15 AA08 AI11 %X ISBN 0-471-90323-X %A Andreas Blass %A Yuri Gurevich %A Dexter Kozen %T A zero-one Law for Logic with a Fixed-point Operator %J Information and Control %V 67 %D 1985 %N 1-3 %P 70-90 %K AI11 %A J. Sakalauskaite %T Axiom Systems for Proving the Equivalence of Compositions of Simple Assignments %J Mat. Logika Primenen. No. 1 %D 1981 %P 109-132 %K AA08 AI11 %X Russian. English and Lithuanian summaries %A A. Prasad Sistla %A Moshe Y. Vardi %A Pierre Wolper %T The Complementation Problem fo Buchi Automata with Applications to Temporal Logic %B BOOK52 %P 465-475 %K AI11 %A Colin Stirling %T A Complete Modal Proof System for a Subset of SCCS %B BOOK47 %P 235-266 %K AA08 AI11 %A Colin Stirling %T A Complete Compositional Modal Proof System for a subset of CCS %B BOOK52 %P 475-486 %K AA08 AI11 %A Rimgaudas Zaldokas %T Construction of Term Rewriting Rules for Abstract Data Types %J Mat. Logika Primenen No. 1 %D 1981 %P 9-19 %K AI14 %A Lev Goldfarb %T A New Approach to Pattern Recognition %B Progress in Pattern Recognition %P 241-402 %S Machine Intell. Pattern Recognition %V 1 %I North-Holland %C Amsterdam, New York %D 1985 %A Jieh Hsiang %A Mandayam Srivas %T PROLOG-based Inductive Theorem Proving %B BOOK40 %P 129-149 %K AI10 AI11 %A Neil D. Jones %A Alan Mycroft %T Stepwise Development of Operational and Denotational Semantics of Prolog %B BOOK50 %P 281-288 %K AI10 AI11 O02 %A Kenneth M. Kahn %T A Primitive for the Control of Logic Programs %B BOOK50 %P 242-251 %K AI10 %A Prateek MIshra %T Towards a Theory of Types in Prolog %B BOOK50 %P 289-298 %K AI10 O02 %A David A. Plaisted %T The Occur-Check Problem in Prolog %B BOOK50 %P 272-280 %K AI10 %A Zbigniew Ras %A Maria Zemankova-Leech %T Rough Sets Based Learning Systems %B Computation Theory (Zaborow, 1984) %S Lecture Notes in Computer Science %V 275 %I Springer-Verlag %C Berlin-Heidelberg-New York %D 1985 %P 263-275 %K AI04 %A Mark E. Stickel %T A PROLOG Technology Theorem Prover %B BOOK50 %P 211-217 %K T02 AI11 %A Hisao Tamaki %T Semantics of a Logic Programming Language with a Reducability Predicate %B BOOK50 %P 259-264 %K AI10 %A Raymond Turner %T Logics for Artificial Intelligence %I Ellis Horwood %C Chichester %D 1985 %A Michael J. Wise %A David M. W. Powers %T Indexing Prolog Clauses Via Superimposed Code Words and Field Encoded Words %B BOOK50 %P 203-210 %K T02 %A Kathy Yelick %T Combining Unification Algorithms for Confined Regular Equational Theories %J BOOK54 %P 365-380 %K AI14 %A Pierre Rety %A Claude Kirchner %A Helene Kirchner %A Pierre Lescanne %T NARROWER: A New Algorithm for Unification and its Application to Logic Programming %J BOOK54 %P 141-157 %K AI10 AI11 %A Harvey Abramson %T Definite Clause Translation Grammars %B BOOK50 %P 233-240 %K AI11 %A Marta Cialdea %T Some Remarks on the Possibility of Extending Resolution Proof Procedures to Intuitionistic Logic %J Inform. Process. Lett %V 22 %D 1986 %N 2 %P 87-90 %K AI10 AI11 %A Stavros S. Cosmadakis %A Paris C. Kanellakis %T Two Applications of Equational Theories to Database Theory %B BOOK54 %P 107-123 %K AI10 AA09 AI11 %A Amitava Bagchi %A A. Mahanti %T Three Approaches to Heuristic Search in Networks %J JACM %V 32 %D 1985 %N 1 %P 1-27 %K AI03 %A G. Gottlob %A A. Leitsch %T On the Efficiency of Subsumption Algorithms %J JACM %V 32 %D 1985 %N 2 %P 280-295 %K AI11 %A O. K. Khanmamedov %T Approximating Perceptron and Convergence of a Process of Training a Classifier %J Akad. Nauk. Azerbaidzhan. SSR Dokl. %V 41 %D 1985 %N 8 %P 8-11 %K AI04 AI06 %X Russian with English and Azerbaijani Summaries %A V. S. Neiman %T Unattainable Subgoals in Searching for an Inference from a Goal %B Complexity Problems of Mathematical Logic %P 68-72 %I Kalinin. Gos. Univ. %C Kalinin %D 1985 %K AI16 %X Russian %A Bernard Silver %T Meta-level Inference. Representing and Learning Control Information in Artificial Intelligence %S Studies in Computer Science and Artificial Intelligence %V 1 %I North-Holland Publishing Co. %C Amsterdam-new York %D 1986 %K AT15 AI04 AI03 AI16 %X ISBN-0-444-87900-5 %A V. I. Vasilev %A F. P. Ovsyannikova %T Optimization of the Space in Teaching Pattern Recognition %J Soviet J. Automat. Inform. Sci %D 1985 %N 3 %P 6-14 %V 18 %K AI04 AI06 %A Dennis de Champeaux %T About the Paterson-Wegman Linear Unification Algorithm %J J. Comput. System Sci %V 32 %D 1986 %N 1 %P 79-90 %K AI11 %A Da Fa Li %T Semantic Resolution and Paramodulation for Horn Sets %J J. Huazhong Univ. Sci. Tech %V 12 %N 2 %P 13-16 %K AI10 AI11 %X Chinese with English Summary %A David Harel %T Dynamic Logic %B Handbook of Philosophical Logic, Vol II %P 497-604 %S Synthese Library %V 165 %C Reidel, Boston %D 1984 %A A. Hoppe %T Temporal Logic Specification of Synchronization Primitives %B BOOK55 %P 455-466 %K AA08 %A Erica Jen %T Invariant Strings and Pattern-Recognizing Properties of One-Dimensional Cellular Automata %J J. Statist. Phys %V 43 %D 1986 %N 1-2 %P 219-242 %K AI12 %A H. R. Nielson %T A Hoare-Like Proof System for Total Correctness of Nested Recursive Procedures %B BOOK55 %P 227-239 %K AA08 %A Shi Tie Wang %T Modal Logic and Program Verification %J Acta Sci. Natur. Univ. Amoien %V 24 %D 1985 %N 3 %P 300-307 %K AA08 %X Chinese with English Summary %A S. J. Young %A C. Proctor %T UFI - An Experimental Frame Language Based on Abstract Data Types %J The Computer Journal %V 29 %N 4 %D AUG 1986 %P 340-347 %K AI16 %A H. J. Eibner %A D. Holzel %T Aspects of Expert Systems Applications in Medicine %J Angewandte Informatik %N 7 %D JUL 1986 %P 279-284 %K AI01 AA01 %A Q Tian %A Michael N. Huhns %T Algorithms for Subpixel Registration %J MAG86 %P 220-233 %K AI06 %A Vladimir Kim %A Leonid Yaroslavskii %T Rank Algorithms for Picture Processing %J MAG86 %P 234-258 %K AI06 %A Michael H. Brill %T Perception of Transparency in Man and Machine: A Comment on Beck %J MAG86 %P 270-271 %K AI06 AI08 %A Ye. K. Gordiyenko %A V. N. Zakhavov %T Process Management in Knowledge Bases %J Soviet Journal of Computer and Systems Sciences %V 24 %N 1 %D JAN-FEB 1986 %P 81-95 %K H03 %A C. L. Ramsey %A J. A. Reggia %A D. S. Nau %A A. Ferrentino %T A Comparative Analysis of Methods for Expert Systems %J International Journal of Man-Machine Studies %V 24 %N 5 %D MAY 1986 %K AI01 %P 475 %K AI01 %A Bruce L. Golden %A A. Hevner %A D. Power %T Decision Insight Systems for Microcomputers: a Critical Evaluation %J MAG87 %P 287-300 %K AI13 %A Arjang A. Assad %A Bruce L. Golden %T Expert Systems, Microcomputers and Operations Research %J MAG87 %P 301-322 %K H01 AI01 %A Jeffrey Perrone %T Down from the Clouds: Notes on "Expert Systems, Microcomputers, and Operation s Research" %J MAG87 %P 323-324 %K H01 AI01 %A E. Eugene Carter %T Creating a Shell-based Expert System %J MAG87 %P 325-328 %K T03 AI01 %A James A. Reggia %A Sanjesv B. Ahuja %T Selecting an Approach to Knowledge Processing %J MAG87 %P 329-332 %K AI01 %A Richard T. Wong %T Comment on "Expert Systems, Microcomputers, and Operations Research" %J MAG87 %P 333 %K AI01 ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:10:13 1986 Date: Mon, 20 Oct 86 03:09:53 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #223 Status: R AIList Digest Friday, 17 Oct 1986 Volume 4 : Issue 223 Today's Topics: Bibliography - Leff Bibliography Continuation #4 ---------------------------------------------------------------------- Date: WED, 20 apr 86 17:02:23 CDT From: leff%smu@csnet-relay Subject: Bibliography (continued) %A W. H. H. J. Lunscher %A M. P. Beddoes %T Optimal Edge Dector Evaluation %J IEEE Transactions on Systems, Man and Cybernetics %V SMC-16 %N 2 %D MAR/APR 1986 %P 304-312 %K AI06 %A L. F. Chaparro %A M. Boudaoud %T Image Multimodeling and a Two-Dimensional Multicategory Wiener Filter %J IEEE Transactions on Systems, Man and Cybernetics %V SMC-16 %N 2 %D MAR/APR 1986 %P 312-316 %K AI06 %A Mitch Betts %T In with Electronic Filing System, Out with Antique Regulations %J ComputerWorld %D JUL 21, 1986 %V 20 %N 29 %P 15 %K AI02 AA14 AA06 Securities and Exchange Commission SEC Internal Revenue Service IRS %X The Securities and Exchange Commission is requiring companies to do their mandatory filings on computer readable media. The SEC has tried an AI system to extract financial data from the reports to be input into calculations. This worked with a 94 percent success rate but the SEC is now requiring that these figures be tagged for easy extraction. The IRS would like to store tax returns on optical disk and then destroy the paper copies but the Department of Justice is opposed because this would prevent forensic examination of fingerprints or signatures on the physical returns. %A Charles Babcock %T AI to drive 5GL Software %J ComputerWorld %D JUL 21, 1986 %V 20 %N 29 %P 23+ %K George Schussel AA08 AA06 AT14 %X George Schussel, president of Digital Consulting Associates said that AI would become part of fifth generation languages to help automate the programming of business software systems. %A Eddy Goldberg %T Expert System Financial Tool Out for Small Business %J ComputerWorld %D JUL 21, 1986 %V 20 %N 29 %P 28 %K AT02 H01 AA06 Sterling Wentworth Businessplan financial planner %X Sterling Wentworth announced that Businessplan would be released in August. This is a tool for financial planners and contains 7500 decision rules and 500 parameters that can be adjusted by the financial planner for his philosophy and style. It costs $4500 and runs on IBM PC's. %A Leilani Allen %T The Cost of an Expert %J ComputerWorld %D JUL 21, 1986 %V 20 %N 29 %P 59-68 %K Knowledge Consortium Campbell's Soup Company AI01 %X quantifies the cost of a human expert in salary, overhead, etc., so that people can judge whether building an expert system to replace him is worth the investment. %T Tool Lets PC, 370 Share Applications %J ComputerWorld %D JUL 21, 1986 %V 20 %N 29 %P 81 %K H01 T03 Aion MVS AI01 %X Aion has two shell products, one for the IBM PC and the other for IBM mainframes under MCS which are fully compatible so that applications can be shared. The MVS version sells for $60,000. %T AI Eases Conversion form CAD to NC Format %J Electronics %D MAR 31, 1986 %P 67-68 %V 59 %N 13 %K AA26 %X PMX is selling an AI system that will convert IGES standard data to numerical control programs. It runs on PC/XT's and costs for $8500, $11,500 for 3d capabilities %A K. W. Ng %A W. Y. Ma %T Pitfalls in Prolog Programming %J SIGPLAN Notices %V 21 %N 4 %D APR 1986 %P 75-79 %K T02 %A Gerardo Cisneros %A Harold V. Mcintosh %T Introduction to the Programming Language Convert %J SIGPLAN Notices %V 21 %N 4 %D APR 1986 %P 48-57 %K H01 %X A new applicative and transformation based language that runs on 8080 and 808 6 based systems %A T. L. Huntsberger %A C. Rangarajan %A S. N. Jayaramamurthy %T Representation of Uncertainty in Computer Vision Using Fuzzy Sets %J IEEE Transactions on Computers %V C-35 %D FEB 1986 %N 2 %P 145-157 %K Flash O04 AI06 %A Takeshi Yamakawa %A Tsutomu MIki %T The Current Mode Fuzzy Logic Integrated Circuits Fabricated by the Standard CMOS Process %J IEEE Transactions on Computers %V C-35 %D FEB 1986 %N 2 %P 161-167 %K O04 %T Olivetti, Digtalk Ink Pact to Make AI Pack Version %J Electronic News %D MAY 19, 1986 %V 32 %N 1602 %P 44 %K Smalltalk H01 AT16 AI01 %X Olivetti has agreed with Digitalk to jointly develop an advanced version of Digitalk's Smalltalk for 80286 based systems. Olivetti will use the system as the primary expert in its Advance Technology Center and will integrate the resulting products with a proprietary environment. %A Eddy Goldberg %T Massively Parallel Processor Introduced %J Computerworld %D May 5, 1986 %V 20 %N 18 %P 4 %K connection machine AT02 H03 %X announcement thereof. They have sold six units. Applications demonstrated include document retrieval fluid dynamics modelling, creating contour maps from aerial photographs and VLSI design %A Eric Bender %T HAL: Just Another Add on %J Computerworld %D May 5, 1986 %V 20 %N 18 %P 19+ %K Lotus AI02 H01 AA15 %X HAL, an English language interface, to Lotus 1-2-3 offers transcripts, unddo commands, self-documenting English language macros, %A Barbara Robertson %T AI Typists Now Rates Satisfactory for Novices %J INfoWorld %D MAY 19, 1986 %V 8 %N 20 %P 63-64 %K AA15 AT02 H01 AT07 AT03 %X A review of an updated version of this Word Processor, alledgedly using AI to help with correcting spelling errors. It got the following ratings: .DS L Overall: 5.4 Performance: Satisfactory Documentation: Poor Ease of Learning: Very Good Ease of Use: Very Good Error Handling: Satisfactory Support: Very Good Value: Satisfactory .DE %A Hank Kee %T PC Managers Should Not Consider AI a Panacea %J INfoWorld %D MAY 19, 1986 %V 8 %N 20 %P 34 %K AI01 %X column argues that we need more applications and less shells and that AI shells should be oriented towards the non-programmer. [See August Spang Robinson Report for a list of real Expert Systems that are being used. LEFF] %T Expert Systems Firm Gets Boost %J Electronics %D MAY 5, 1986 %V 59 %N 19 %P 64 %K France AI01 AT16 Cognitech AA05 %X Cognitech got four million dollars of additional capital. They got 53 expert system orders including one from Pechiney for a system analyzing faults in cast aluminum. %A D. D. Kary %A P. L. Juell %T TRC; An Expert System Compiler %J Sigplan Notices %V 21 %N 5 %D MAY 1986 %P 64-68 %K air cargo C T03 %X describes an expert system tool which translates input into C code. It has been applied to an air cargo routing problem. When the system was translated from a LISP based expert system tool running on the VAX to TRC, the execution time went down from hours to seconds. The system consists of an expert system running in conjunction with a mathematical optimization technique. The expert system handles things like incompatibilities between objects. The results are as good or better than human experts. %A J. B. Marti %A A. C. Hearn %T REDUCE as a Lisp Benchmark %J MAG60 %P 8-16 %K T01 %X CPU times for various machines running the REDUCE timing test. REDUCE is a symbolic math package written in LISP. (There are more machines and other information in the article) .TS tab(~); l n. Amdahl 470 V8~7.2 Apollo DN 600~89.9 CDC Cyber 170/825~106.8 DEC 1099~17.7 DEC 2020~122.8 DEC 2060~22.5 DEC VAX 11/750~78.7 DEC VAX 11/780~50.3 Facom M-382~3.6 Hewlett Packard 9836~65.3 Hitachi S-810~2.8 IBM 3031~40.1 IBM 3084~5.4 IBM 4341 MOdel 1~52.0 IBM 4341 Model 2~30.1 Robotron ES-1040~149.2 Sage IV~224.8 Siemens 7890~3.8 SML Darkstar~227.9 Symbolics 3600~45.0 Tektronix 4404~120.1 Xerox Dolphin~322.0 .TE %A J. W. Shavlik %A G. F. DeJong %T Computer Understanding and Generalization of Symbolic Mathematical Calculations: A Case Study in Physics Problem Solving %J MAG61 %P 148-153 %K AA16 AA07 AI04 %A M. Hadzikadic %A F. Lichtenberger %A D. Y. Y. Yun %T An Application of Knowledge-Base Technology in Education: A Geometry Theorem Prover %J MAG61 %P 141-147 %K AA13 AA07 T02 H01 %A J. S. Vitter %A R. A. Simons %T New Classes for Parallel Complexity: A Study of Unification and Other Complete Problems for P [Script P] %J IEEE Transactions on Computers %D MAY 1986 %V C-35 %N 5 %P 403-418 %K AI11 O06 H03 %X Parallel Algorithms for Unification in $O ( E over P + V log P)$ or $O( alpha (2E,V) E over P + V )$ where E is the number of edges and V is the number of vertices in the expression graph and P is the number of processor and $alpha$ is the inverse ackerman's fun ction. %A Karen Sorensen %T AT&T Leads New Scanner Parade %J InfoWorld %V 8 %N 19 %D MAY 12, 1986 %P 17 %K AI06 Vision Research Canon %X AT&T has an Image Director for digitizing an 8.5 by 11 paper at 100 by 100 resolution for $2885.00 Vision Research has a 8.5 by 11 scanner for $2495.00. Canon has a scanner for $1,190. OCR software is available for $595.00 to go with it. %T Study Says Australia Needs AI Development %J InfoWorld %V 8 %N 19 %D MAY 12, 1986 %P 30 %K AI16 %X According to an Australian government report, Australia has an international strength in expert systems but needs help to commercialize their work. %A Charles Babcock %T Cobol-Based AI Shell Bows %J ComputerWorld %D SEP 1, 1986 %V 20 %N 35 %K McCormick and Dodge John B. Landry Distribution Management Systems T03 AI01 AT02 %X Distribution Management Systems (DMS) will be producing expert system shells written in Cobol designed to be integrated into mainstream MIS. Releases scheduled are DEC for October, MVS/CICS in January and the IBM PC for first quarter 1987. %T Fujitsui Commits to AI Market %J ComputerWorld %D SEP 1, 1986 %V 20 %N 35 %P 15 %K AT04 AT02 H01 T03 AI01 GA01 %X Fujitsu has an Expert System shell running on its FM 16 Beta PC costing $2940 and oriented to the Japanese Language %A Michael Sullivan-Trainor %T In Depth %J ComputerWorld %D SEP 1, 1986 %V 20 %N 35 %P 55-62 %K AI01 AA18 AA06 Perks budget support personnel %X describes development and functionality of expert system for budget analysis for the US Navy and one to help Army force designers design how many support personnel are needed. %A Maura McEnaney %T Lefebvre Signs on with Expert Systems Developer Cognitive %J ComputerWorld %D SEP 1, 1986 %V 20 %N 35 %P 95 %K AI01 AT11 AT16 Multimate %X Richard Lefebvre, former chief operating officer for Multimate, is now president and ceo of Cognitive Systems Inc.. %T Japanese Launch Language Project %J InfoWorld %D SEP 1, 1986 %V 8 %N 35 %P 18 %K AI02 GA01 Hitachi NEC Fujitsu Thailand Chinese %X MITI will launch a 7 year 39 million effort to develop translation systems between Japanese and other Asian languages. %T The Next Revolution %J ComputerWorld %D SEP 15, 1986 %V 20 %N 37 %P 16 %K AA06 AT22 %X Editorial stating: The recent announcement of an expert system shell written in COBOL and designed to be integrated into mainframe applications signals the widespread integration of artificial intellgence into MIS. MIS managers should have their existing staffs get involved in AI and look out for applications of AI to their shops. They should be "movers" and not "responders" during this next phase of the computer revolution. [. ComputerWorld is a weekly newspaper with one of the largest circulations in the computer commuity LEFF .] %A Harvey P. Newquist %T Forty-bit Architecture: Latest in Push for More Power %J ComputerWorld %D SEP 15, 1986 %V 20 %N 37 %P 17 %K H02 Integrated Inference SM45000 %X discusses the new Integrated Inference Machines SM45000 which uses a 40 bit word %T Artificial -intelligence Work Gains Mainstream Acceptance %J The Institute %V 10 %N 10 %P 1+ %D OCT 1986 %K AI07 Nils Nillson Feigenbaum Minsky McDermott Schorr IBM AT14 %X discusses statements by Edward Feigenbaum, Marvin Minsky, Drew McDermott and Nils Nillson at the recent AAAI conference. Edward Feigenbaum "claimed that every time an area of AI becomes successful, it is no longer considered Artificial Intelligence." Marvin Minsky said that there isn't necessarily anything corresponding to "intelligence." Nils Nilsson discussed robotics. They applauded IBM for its "embracing" of AI and for acknowledging university research. However, there was a complaint about a long conversation with IBM represenatives in which they asked what new techniques that would have applications to new products would come out of MIT in the next eighteen months. %A Charles Babcock %T Landry Returns to the Fray %J ComputerWorld %D SEP 8, 1986 %V 20 %N 36 %P 19+ %K John Landry McCormack and Dodge Distribution Management Systems AA06 %X Discusses the head of Impact/AE which is the expert system for use in COBOL environments. %A Eddy Goldberg %T AI Debuts Move Expert Systems into Mainstream Computing %J ComputerWorld %D SEP 8, 1986 %V 20 %N 36 %P 22 %K AAAI-86 Xerox CommonLoops Texas INstruments Vaxstation DEC Apollo Franz Aion Lisp Machine H02 H01 T03 T01 %X reviews some announcements made at AAAI-86 %A MItch Betts %T Archives Gets Expert System %J ComputerWorld %D SEP 8, 1986 %V 20 %N 36 %P 93 %K AA14 %X The National ARchives is developing a prototype expert system to help users who have vague requests for information. In tests, the computer and the archivi sts agreed 66% of the time, 21% of the answers were achieved by the computer and not the archivists and 13% of the time the archivists gave the answer and not the computer and 7% of the time the computer was "simply wrong." The prototype covers the old Bureau of Land Management records and was written with M.1 %T DEC Unveils HIgh-end VAX %J ComputerWorld %D SEP 8, 1986 %V 20 %N 36 %P 107 %K DEC T01 %X DEC introduced the AI Vaxstation/GPX which is a color version of the Microvax II. %T New Products/Software and Services %J ComputerWorld %D SEP 8, 1986 %V 20 %N 36 %P 111 %K T03 OPS5 Data Directions DDi-OPS Xerox 1100 %X Data Directions, 37 Jerome Ave., Bloomfield, Conn 06002, has released an OPS-5 for the Xerox Corp. 1100 Lisp Machine costing $995.00 %A Alice LaPlante %T Communications Program to Help Novices, Experts %J infoWorld %D SEP 8, 1986 %V 8 %N 36 %P 16 %K AA08 AI01 H01 AA15 %X Costing $49.95, is a system that helps users handling microcomputer communications. It helps configure Smartcom II, Crosstalk, Concept Development' s Line Plus. It can help design a serial cable. %T Symbolics Compiler Gets DOD Approval %J Electronic News %V 32 %N 1618 %D SEP 8, 1986 %P 26 %K H02 Symbolics Tempest Ada %X DOD validated Symbolics' ADA compiler. This costs $3600.00. They also brought out a Tempest version of their 3645 processor which will cost $104,900 %T Commercializing AI Provides 16000 Jobs in US %J Electronics %D SEP 18, 1986 %P 23 %V 59 %N 13 %K AT04 %X There are 16000 people now working in US to commercialize AI technologies. This excludes people in academe and research organizations. %T Vision Processor on a Board Goes for $10,000 %J Electronics %D SEP 18, 1986 %P 28 %V 59 %N 13 %K AI06 AT02 %X The 2000/VP costs $10,000 and is said to be comparable to $40,000 boards %T Dainichi Kiko Asks Court Protection %J Electronics %D SEP 18, 1986 %P 114 %V 59 %N 13 %K AI07 AT16 GA01 %X This company, a fast growing Japanese robotic maker, sought court protection from creditors. %T Vision System Checks Assembled Boards %J Electronics %D SEP 18, 1986 %P 103 %V 59 %N 13 %K AI06 AA26 AA04 %X Intellidex has a new system that uses ten cameras to check PC boards. %A Peggy Watt %T AI Languages for Mac, IBM PC,VAX Introduced %J ComputerWorld %D SEP 22, 1986 %V 20 %N 38 %P 35 %K T02 H01 T01 AT02 logo macintosh VAX %X Expertelligence will be selling an IBM PC version of prolog and versions of LISP for Macintosh and VAX. The system uses Macintosh like windows and pull down menus. %A Pat Shipman %T The Recent Life of an Ancient Dinosaur %J Discover %D OCT 1986 %V 7 %N 10 %K analogy functional anatomy AA10 %X shows where reasoning by analogy went wrong and where it went right in determining the nature of the Iguanadon from various bones. He argues that analogy from functional similarities is valid but that from mere circumstantial reasoning where there is no causual relationship between the sets of traits in question. Might be of interest to those developing AI systems to reason by analogy. %A D. Snyers %A A. Thayse %T Algorithmic State Machine Design and Automatic Theorem Proving: Two Dual Approaches to the Same Activity %J IEEE Transactions on Computers %V C-35 %N 10 %D OCT 1986 %V C-35 %K AI11 AA08 %X Transformations acting on P-function can be interpreted in terms of synthesizing programs consisting of if-then-else and do and theorem proving. Attempts to show a relation between Prolog and logic design. %A Ralph Emmett Carlyle %T Sneaking in the Back %J Datamation %D OCT 1, 1986 %V 32 %N 19 %P 32+ %K AT02 Cullinet MSA McCormack and Dodge AA06 MIS Impact/AE Distribution Management Incorporated AION CICS %X MSA is adding rules-based software to Information Expert and will be announcing stand alone systems for expert systems. Boole and Babbage and others will be adding expert systems to their performance measurement and capacity planning systems. Aion will be able to run its expert systems under CICS. (Interview with Bob goldman of Artificial Intelligence Corporation which markets Intellect. They are developing expert systems, voice recognition systems and software to run under IBM's DB2 database system.) %A James T. Brady %T A Theory of Productivity in the Creative Process %J IEEE Computer Graphics and Applications %D May 1986 %V 6 %N 5 %P 25-34 %K AI08 roll system response time %X discusses the state of "being on a roll" where everything seems to go right. Programmers and engineers who used terminals found that for programmers system response time dropping from 2.5 to .3 seconds increased productivity by a factor of two and for engineers in a graphic applications environment, productivity could go up as much as nine times for a drop from 1.5 secons to 0.3 seconds. Develops an analytical model to explain these empirical results. %A Ellis S. Cohen %A Edward t. Smith %A Lee A. Iverson %T Constraint Based Tiled Windows %J IEEE Computer Graphics and Applications %D May 1986 %V 6 %N 5 %P 35-45 %K AI15 AI01 %X discusses uses ruled based techniques to automatically determine size and locations of windows in a tile-based systems. This is where windows do not "overlap" but are resized so they all fit together on the rectangle that forms the screen. %A G. J. Li %A B. W. Wah %T Coping with Anomalies in Parallel Branch-and-Bound Algorithms %J IEEE Transactions on Computers %D JUN 1986 %V C-35 %N 6 %P 568-573 %K H03 AI03 %X Sufficient conditions to guarantee no degradation in performance due to parallelisma nd necessary conditions for allowing parallelism to have a speedup greater than the number of processors is found. %A Ed Winfield %T Image Processing Prooducts for the Q=Bus Meet INdustry Needs for Precision Inspection %J Hardcopy %V 6 %N 7 %D JUL 1986 %P 83-94 %K AI06 AT02 %X Data Translation .br DT2651 Frame Grabber 512 x 512, on-board ALU .br Datacube .br QVG-153 768x512 x 8 bit frame capture, can be expanded to 24 bits per pixel, daughterboard to do processing .br Matrox .br QFAF-512 512 x 512 x 4 .br Reticon .br SB6320 (interface to reticon's solid-state cameras) .br Imaging Technology .br AP512,FB512,ALU512, processor, display and converter for 512 by 512 (hardware for histograms and feature extractions %A John Naughton %T Artificial Intelligence: Can DEC Stay Ahead? %J Hardcopy %V 6 %N 7 %D JUL 1986 %P 113-117 %K AI01 AA26 AA21 %X IDT helps engineers locate field-replaceable units in PDP 11-03's. (description of other of DEC's AI expert systems and experiences.) %A T. F. Knoll %A R. C. Jain %T Recognizing Partially Visible Objects Using Feature Indexed Hypotheses %J IEEE Journal of Robotics and Automation %D MAR 1986 %V RA-2 %N 1 %P 3-13 %K AI06 %X develops an algorithm for isolating objects that partially match of cost $O( sqrt p ) r$ where p is the number of possible objects and r is the number of redundancies. %A E. K. Wong %A K. S. Fu %T A Hierarchical Orthoganal Space Approach to Three-Dimensional Path Planning %J IEEE Journal of Robotics and Automation %D MAR 1986 %V RA-2 %N 1 %P 42-52 %K AI07 AI03 AI09 ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:12:30 1986 Date: Mon, 20 Oct 86 03:12:20 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe Subject: AIList Digest V4 #224 Status: R AIList Digest Sunday, 19 Oct 1986 Volume 4 : Issue 224 Today's Topics: Mathematics - PowerMath, Learning - Neural Networks & Connectionist References, Expert Systems - ESE ---------------------------------------------------------------------- Date: 15 Oct 86 19:30:47 GMT From: hao!bill@seismo.css.gov (Bill Roberts) Subject: Algebraic manipulators for the Mac Has anyone in netland heard of any algebraic manipulator systems for the MacIntosh? I recently saw were a company called Industrial Computations Inc. of Wellesley, MA is marketing a program called "PowerMath". The ad reads Type in your problem, using conventional math notation, and PowerMath will solve your calculus, algebra and matrix problems. PowerMath does factorials, summations, simultaneous equations, plots, Taylor series, trigonometry and allows unlimited number size. That last statement ("...unlimited number size.") hints at PowerMath being a symbolic computation engine as opposed to an equation solver like TKSolver. Thanks in advance for any input. Bill Roberts NCAR/HAO Boulder, CO !hao!bill "...most people spend their lives avoiding intense situations, a Repo man seeks them out!" ------------------------------ Date: 12 Oct 86 23:10:00 GMT From: uiucuxa!lenzini@uxc.cso.uiuc.edu Subject: To: Bob Caviness To : Bob Caviness Sorry about this posting but I can't seem to get through to Bob Caviness at the University of Del. Here are a couple of integrals that you can cut MACSYMA loose on. I've been trying to use the program myself but the results I've been getting are unbelievably complex (Read 8 page constants that I can't seem to simplify). Hopefully you have expanded the integration capabilities enough to handle this. Thanks again. inf / ! !(A + B*(x)^(1/2))^2 + (C*x + B*(x)^(1/2))^2 D I = !--------------------------------------------- * ----------- cos(E*x)dx 1 !(A + B*(x)^(1/2))^2 + (x + B*(x)^(1/2))^2 D^2 + x^2 ! / 0 I = same integral as I without the cos(E*x) term 2 1 Any help would be greatly appreciated. Thanks in advance. Andy Lenzini University of Illinois. ------------------------------ Date: 17 Oct 86 23:55:03 GMT From: decvax!dartvax!merchant@ucbvax.Berkeley.EDU (Peter Merchant) Subject: Re: Algebraic manipulators for the Mac > ...I recently saw were a company called Industrial Computations Inc. > of Wellesley, MA is marketing a program called "PowerMath". The ad reads > > Type in your problem, using conventional math notation, and > PowerMath will solve your calculus, algebra and matrix > problems. PowerMath does factorials, summations, simultaneous > equations, plots, Taylor series, trigonometry and allows > unlimited number size. > > That last statement ("...unlimited number size.") hints at PowerMath being a > symbolic computation engine as opposed to an equation solver like TKSolver. > Thanks in advance for any input. > Bill Roberts I had a chance to use PowerMath and was severely impressed. It does all sorts of mathematical functions and has a very nice Macintosh interface. I have a feeling, though, that is program was originally designed for a mainframe. I would love to see PowerMath run on a Mac with a Prodigy upgrade, or maybe a HyperDrive 2000. I used one on a 512K Mac and, while it was very good, was the most slowest (yes, I meant to do that) program I had ever seen. The program took minutes to do what TK!Solver seconds. On the other hand, it did do everything it advertised. Made good graphs, too. If time is not a problem for you, I'd really suggest it. If anyone has detes on it running on a Prodigy upgrade, PLEASE LET ME KNOW! -- "Do you want him?! Peter Merchant Or Do you want me!? 'Cause I want you!" ------------------------------ Date: 18 Oct 86 15:00:39 GMT From: clyde!watmath!watnot!watmum!bwchar@caip.rutgers.edu (Bruce Char) Subject: Re: Algebraic manipulators for the Mac There is an article by two of the authors of PowerMath in the Proceedings of the 1986 Symposium on Symbolic and Algebraic Computation (sponsored by ACM SIGSAM): "PowerMath, A System for the Macintosh", by J. Davenport and C. Roth, pp. 13-15. Abstract from the paper: PowerMath is a symbolic algebra system for the MacIntosh computer. This paper outlines the design decisions that were made during its development, and explains how the novel MacIntosh environment helped and hindered the development of the system. While the interior of PowerMath is fairly conventional, the user interface has many novel features. It is these that make PowerMath not just another microcomputer algebra system. Bruce Char Dept. of Computer Science University of Waterloo ------------------------------ Date: 17 Oct 86 05:34:57 GMT From: iarocci@eneevax.umd.edu (Bill Dorsey) Subject: simulating a neural network Having recently read several interesting articles on the functioning of neurons within the brain, I thought it might be educational to write a program to simulate their functioning. Being somewhat of a newcomer to the field of artificial intelligence, my approach may be all wrong, but if it is, I'd certainly like to know how and why. The program simulates a network of 1000 neurons. Any more than 1000 slows the machine down excessively. Each neuron is connected to about 10 other neurons. This choice was rather arbitrary, but I figured the number of connections would be proportional the the cube root of the number of neurons since the brain is a three-dimensional object. For those not familiar with the basic functioning of a neuron, as I under- stand it, it functions as follows: Each neuron has many inputs coming from other neurons and its output is connected to many other neurons. Pulses coming from other neurons add or subtract to its potential. When the pot- ential exceeds some threshold, the neuron fires and produces a pulse. To further complicate matters, any existing potential on the neuron drains away according to some time constant. In order to simplify the program, I took several short-cuts in the current version of the program. I assumed that all the neurons had the same threshold, and that they all had the same time constant. Setting these values randomly didn't seem like a good idea, so I just picked values that seemed reasonable, and played around with them a little. One further note should be made about the network. For lack of a good idea on how to organize all the connections between neurons, I simply connect- ed them to each other randomly. Furthermore, the determination of whether a neuron produces a positive or negative pulse is made randomly at this point. In order to test out the functioning of this network, I created a simple environment and several inputs/outputs for the network. The environment is simply some type of maze bounded on all sides by walls. The outputs are (1) move north, (2) move south, (3) move west, (4) move east. The inputs are (1) you bumped into something, (2) there's a wall to the north, (3) wall to the south, (4) wall to the west, (5) wall to the east. When the neuron corresponding to a particular output fires, that action is taken. When a specific input condition is met, a pulse is added to the neuron corresponding to the particular input. The initial results have been interesting, but indicate that more work needs to be done. The neuron network indeed shows continuous activity, with neurons changing state regularly (but not periodically). The robot (!) moves around the screen generally winding up in a corner somewhere where it occas- ionally wanders a short distance away before returning. I'm curious if anyone can think of a way for me to produce positive and negative feedback instead of just feedback. An analogy would be pleasure versus pain in humans. What I'd like to do is provide negative feedback when the robot hits a wall, and positive feedback when it doesn't. I'm hoping that the robot will eventually 'learn' to roam around the maze with- out hitting any of the walls (i.e. learn to use its senses). I'm sure there are more conventional ai programs which can accomplish this same task, but my purpose here is to try to successfully simulate a network of neurons and see if it can be applied to solve simple problems involving learning/intelligence. If anyone has any other ideas for which I may test it, I'd be happy to hear from you. Furthermore, if anyone is interested in seeing the source code, I'd be happy to send it to you. It's written in C and runs on an Atari ST computer, though it could be easily be modified to run on almost any machine with a C compiler (the faster it is, the more neurons you can simulate reasonably). [See Dave Touretzky's message about connectionist references. -- KIL] -- | Bill Dorsey | | 'Imagination is more important than knowledge.' | | - Albert Einstein | | ARPA : iarocci@eneevax.umd.edu | | UUCP : [seismo,allegra,rlgvax]!umcp-cs!eneevax!iarocci | ------------------------------ Date: 15 Oct 86 21:12 EDT From: Dave.Touretzky@A.CS.CMU.EDU Subject: the definitive connectionist reference The definitive book on connectionism (as of 1986) has just been published by MIT Press. It's called "Parallel Distributed Processing: Explorations in the Microstructure of Cognition", by David E. Rumelhart, James H. McClelland, and the PDP research group. If you want to know about connectionist models, this is the book to read. It comes in two volumes, at about $45 for the set. For other connectionist material, see the proceedings of IJCAI-85 and the 1986 Cognitive Science Conference, and the January '85 issue of the journal Cognitive Science. -- Dave Touretzky PS: NO, CONNECTIONISM IS NOT THE SAME AS PERCEPTRONS. Perceptrons were single-layer learning machines, meaning they had an input layer and an output layer, with exactly one learning layer in between. No feedback paths were permitted between units -- a severe limitation. The learning algorithms were simple. Minsky and Papert wrote a well-known book showing that perceptrons couldn't do very much at all. They can't even learn the XOR function. Since they had initially been the subject of incredible amounts of hype, the fall of perceptrons left all of neural network research in deep disrepute among AI researchers for almost two decades. In contrast to perceptrons, connectionist models have unrestricted connectivity, meaning they are rich in feedback paths. They have rather sophistcated learning rules, some of which are based on statistical mechanics (the Boltzmann machine learning algorithm) or information theoretic measures (G-maximization learning). These models have been enriched by recent work in physics (e.g., Hopfield's analogy to spin glasses), computer science (simulated annealing search, invented by Kirkpatrick and adapted to neural nets by Hinton and Sejnowski), and neuroscience (work on coarse coding, fast weights, pre-synaptic facilitation, and so on.) Many connectionist models perform cognitive tasks (i.e., tasks related to symbol processing) rather than pattern recognition; perceptrons were mostly used for pattern recognition. Connectionist models can explain certain psychological phenomena that other models can't; for an example, see McClelland and Rumelhart's word recognition model. The brain is a connectionist model. It is not a perceptron. Perhaps the current interest in connectionist models is just a passing fad. Some folks are predicting that connectionism will turn out to be another spectacular flop -- Perceptrons II. At the other extreme, some feel the initial successes of ``the new connectionists'' may signal the beginning of a revolution in AI. Read the journals and decide for yourself. ------------------------------ Date: 15 Oct 86 06:17:23 GMT From: zeus!levin@locus.ucla.edu (Stuart Levine) Subject: Re: Expert System Wanted In article <2200003@osiris> chandra@osiris.CSO.UIUC.EDU writes: >There is an expert system shell for CMS. It is called PRISM. >PRISM is also called ESE (expert system environemnt). >ESE is available from IBM itself. It is written in lisp and was most >probably developed at IBM Watson Research Labs. > Could you give us more info. When we checked into the availability of PRISM, we found that IBM was NOT making it available. It would be interesting to know if that has changed. Also, does it run in LISP (as in a lisp that someone would actually own), or in IBM LISP? ------------------------------ Date: 15 October 1986, 20:54:09 EDT From: "Fredrick J. Damerau" Subject: correction on ESE ESE, (Expert System Environment), is actually PASCAL-based, not LISP, and was developed at the Palo Alto Scientific Center, not Yorktown Research. Fred J. Damerau, IBM Research (Yorktown) ------------------------------ Date: Wed 15 Oct 86 17:05:23-PDT From: Matt Pallakoff Subject: corrections to Navin Chandra note on AIList Digest Navin, I saw your note on IBM's expert system environment (ESE). I worked one summer with the group that developed it. First, it's no longer called PRISM. They changed that fine name, used throughout the research and development, to Expert System Development Environment/ Expert System Consultation Environment, the two subsystems of ESE which are sold separately or together. (I don't think they have reversed this decision since I left.) Secondly, It is written in PASCAL, not LISP. Finally, it was created at the IBM Research Center in Palo Alto, California (where I worked). I don't know a tremendous amount about it (having spent only a couple months working on interfaces to it) but I might be able to give you some general answers to specific questions about it. Matt Pallakoff ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:12:46 1986 Date: Mon, 20 Oct 86 03:12:37 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe Subject: AIList Digest V4 #225 Status: R AIList Digest Sunday, 19 Oct 1986 Volume 4 : Issue 225 Today's Topics: Queries - Statistical Expert Systems & Workshop on AI in Natural Resources and Environmental Planning, Expert Systems - Savior & FRL, Bibliography - Correction ---------------------------------------------------------------------- Date: Fri, 17 Oct 86 09:18:10 SET From: "Adlassnig, Peter" Subject: Statistical expert systems We are interested in building a statistical expert system that is intended to be used by physicians from our Medical School. We would like to obtain information about 1) statistical expert systems in general a) at universities and laboratories b) commercially available systems 2) statistical expert systems in medicine Any information is appreciated. Peter Adlassnig, Department of Medical Computer Sciences, University of Vienna Medical School ------------------------------ Date: 16 Oct 86 14:49:00 GMT From: osiris!chandra@uxc.cso.uiuc.edu Subject: GIS, Environmental, Nat. Resources Applications of AI in NATURAL RESOURCES and ENVIRONMENTAL PLANNING Hi, Recently, Molly Stock (of Univ. of Idaho) conducted a survey of AI applications to forestry management. Her findings are pleasantly surprising. A large number of universities and government agencies are currently building interesting AI applications. Sparked by the survey, we are now planning on holding a NATIONAL WORKSHOP in this area. The purpose of this workshop is to bring researchers together under one roof. We envision this workshop as an opportunity for researchers to share ideas and lay down directions for future research. This letter is a probe. I'm trying to get a sense of "WHAT'S OUT THERE". The areas covered are: - Environmental Planning & management. - Env Impact Statements - Geographic Info Systems - Natural Resource planning - Environmental Modeling - Other related areas If you are involved in any such research and/or would be interested in participating in a WORKSHOP, please contact me at the address below: US-MAIL: D. Navinchandra Intelligent Engineering Systems Lab. Room 1-241 Massachusetts Institute of Technology, Cambridge, MA02139 ARPA-NET: dchandra@athena.mit.edu Phone: (617)577-8047 (call first) (617)253-3880 (call if no answer at above number) If you are currently involved in some projects and or have technical reports, I'd like to know about them. After this survey is complete, a formal Announcement will be promulgated. THANKS D. Navinchandra IESL, MIT (p.s. At the IESL,MIT we are working on building tools to build Knowledge Based systems for Geographic Info Systems. We are doing this research in collaboration with the Environmental group of the Construction Engineering Research Lab, Champaign Illinois) ------------------------------ Date: Thu, 16 Oct 86 10:36:21 BST From: J W T Smith (JWS AT UKACRL) Subject: General purpose ES for VM Rutherford Appleton Laboratory, R1, 2.81, Ext 6487 In response to the request from Linda Littleton of PSU for information on an Expert System for VM/CMS. The Expert System shell called Savior is available for VM. It also runs on PCs, Vaxes, Primes and other minis and micros. We have only used the PC version. The cost (for the VM version) in the UK is 15k pounds but they offer a good educational discount, at least they do in the UK. The producing company is, ISI Ltd 11 Oakdene Road Redhill Surrey RH1 7BT. United Kingdom. Telephone 0737 71327 I'm afraid I don't have a US address for ISI. John Smith. Bitnet: JWS at UKACRL ------------------------------ Date: 16 Oct 86 16:51:00 GMT From: mcvax!ukc!einode!robert@seismo.css.gov (Robert Cochran) Subject: Re: Public Domain Software for Expert Systems >> From: meh@hou2d.UUCP (P.MEHROTRA) >> Date: 11 Oct 86 22:06:27 GMT >> Hi: I am looking for some software available in public domain >> for building expert systems. I work in Unix environment and >> have Franz LISP on my system. I already have OPS5. I am especially >> interested in tools which use frames and/or semantic networks >> for knowledge representation. There is a full implementation of a Frame Representation Language being distributed by a company in Ireland called Generics (Software) Ltd. This FRL runs in any advanced lisp dialect - CommonLISP, FranzLISP, etc. - on machines ranging from an IBM-PC to microVAX and VAX. It's not exactly public domain stuff but it's not expensive either ($300 - $500), and special licences are available for educational institutions. If interested, I suggest you contact them directly for further information at : ....!mcvax!einode!genrix!mcgee. ------------------------------ Date: 16 Oct 86 09:55 EDT From: WAnderson.wbst@Xerox.COM Subject: Incorrect AIList Bibliographic Reference The following reference was passed on to me from one of the AILists (I don't know which one). %A Klaus-Peter Adlassnig %T Fuzzy Set Theory in Medical Diagnosis %J IEEE Transactions on Software Engineering %D NOV 1985 %V SE-11 %N 11 %P 260-265 %K AA01 AI01 O04 %X They developed systems for diagnosing rheumatologic diseases and pancreatic disorders. They achieved 94.5 and 100 percent accuracy, respectively. I tried looking this up in my collection of IEEE Trans on SE, but it's not there. Nov 1985 is an issue on AI, but there is no mention of Fuzzy Set theory. In addition, the Nov 85 issue begins with page 1253. Also, a perusal of the index of the Transactions for 1985 reveals no author with the name Klaus-Peter Adlassnig, and only one entry in the subject index under Fuzzy Sets: "Estimating in correctness of computer program viewed as set of heirarchically structured fuzzy equivalence classes, by F.B. Bastani, Sep 85, pp 857-864." Finally, pages 260 - 265 of Vol SE-11 contain an article by David Parnas, et. al., titled "The Modular Structure of Complex Systems." So here we have an example of an incorrect, online, bibliographic reference. I wonder how many other mistakes are made when this sort of data is entered. This is progress? ("Our entire card catalog is online, but we still can't find anything ...." :-) I wonder if this is in the new IEEE publication on expert systems, Expert Magazine? Bill Anderson ------------------------------ Date: WED, 10 JAN 84 17:02:23 CDT From: E1AR0002%SMUVM1.BITNET@WISCVM.WISC.EDU Subject: mistake The corrected citations are indicated below. Sadly, several references from an issue of IEEE Transactions on Systems, Man and Cybernetics got misattributed to an issue of IEEE Transactions on Software Engineering within ai.bib36 due to an editing mistake. Of the 2600 references sent out in this format, this is first complaint I got about an error in one of the citations. Of the three complaints I got about mistakes in a summary of something I sent in, only one turned out to be my fault. The other two statements were checked successfully against the article in question so the error was on the part of the original author. I thus consider my error rate reasonable. Typing in references by hand is something that I will probably only be doing for a few more years. I suspect by then, I will get an optical disk with all the journals on them and extract the informaiton directly. One publisher is already putting a bar-code like strip with the table of contents in issues of their magazines. I wonder whether it would be legal for someone to get a selective dissemination service from a database provider like Dialog or ISI and pipe that into AIList. I believe SIGART publishes the result of a search of dissertation abstracts for AI related material on a regular basis and SIGPLAN used to do the same for the NTIS database for programming language materials. __________________________________________________________________________ %A G. R. Dattatreya %A L. N. Kanal %T Adaptive Pattern Recognition with Random Costs and Its Applications to Decision Trees %J IEEE Transactions on Systems, Man and Cybernetics %D MAR/APR 1986 %V SMC-16 %N 2 %P 208-218 %K AI06 AA01 AI04 AI01 clustering spina bifida bladder radiology %X applies clustering algorithm to results of reading radiographs of the bladder. The system was able to determine clusters that corresponded to those of patients with spina bifida. %A Klaus-Peter Adlassnig %T Fuzzy Set Theory in Medical Diagnosis %J IEEE Transactions on Systems, Man and Cybernetics %D MAR/APR 1986 %V SMC-16 %N 2 %K AA01 AI01 O04 %X They developed systems for diagnosing rheumatologic diseases and pancreatic disorders. They achieved 94.5 and 100 percent accuracy, respectively. %A William E. Pracht %T GISMO: A Visual PRoblem Structuring and Knowledge-Organization Tool %J IEEE Transactions on Systems, Man and Cybernetics %D MAR/APR 1986 %V SMC-16 %N 2 %P 265-270 %K AI13 AI08 Witkin Geft AA06 %X discusses the use of a system for displaying effect diagrams on decision making in a simulated business environment. The tool improved net income production. The tool provided more assistance to those who were more analytical than to those who used heuristic reasoning as measured by the Witkin GEFT. %A Henri Farreny %A Henri Prade %T Default and Inexact Reasoning with Possiblity Degrees %J IEEE Transactions on Systems, Man and Cybernetics %D MAR/APR 1986 %V SMC-16 %N 2 %P 270-276 %K O04 AI01 AA06 %X discusses storing for each proposition, a pair consisting of the probability that it is true and probability that it is false where these two probabilities do not necessarily add up to 1. Inference rules have been developed for such a system including analogs to modus ponens, modus tollens and how to combine two such ordered pairs applying to the same fact. These have been applied to an expert system in financial analysis. %A Chelsea C. White, III %A Edward A. Sykes %T A User Preference Guided Approach to Conflict Resolution in Rule-Based Expert Systems %J IEEE Transactions on Systems, Man and Cybernetics %D MAR/APR 1986 %V SMC-16 %N 2 %P 276-278 %K AI01 multiattribute utility theory %X discusses an application of multiattribute utility theory to resolve conflicts between rules in an expert system. ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:13:02 1986 Date: Mon, 20 Oct 86 03:12:55 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe Subject: AIList Digest V4 #226 Status: R AIList Digest Sunday, 19 Oct 1986 Volume 4 : Issue 226 Today's Topics: Logic Programming - Proof of TMS Termination, Philosophy - Review of Nagel's Book & Searle, Turing, Symbols, Categories ---------------------------------------------------------------------- Date: Thu, 16 Oct 86 09:20 EDT From: David A. McAllester Subject: TMS Query Response I saw a recent message concerning the termination of belief revision in a Doyle-style TMS. Some time ago I proved that determining the existence of a fixed point for a set of Doyle justifications is NP-complete. It is possible to give a procedure that terminates but all such procedures will have exponential worst case behaviour. The proof is given below: *********************************************************** DEFINITIONS: A NM-JUSTIFICATION is an "implication" of the form: (IN-DEPENDENCIES, OUT-DEPENDENCIES) => N where IN-DEPENDENCIES and OUT-DEPENDENCIES are sets of nodes and N is the justified node. A labeling L marks every node as either "in" or "out". An nm-justification is said to be "active" under a labeling L if every out dependency in the justification is labeled "out" and every in dependency of the justification is labeled "in". Let J be a set of nm-justifications and L be a labeling. We say that a node n is JUSTIFIED under J and L if there is some justification for n which is active under the labeling L. A set J of nm-justifications will be called Doyle Satisfiable if there is a labeling L such that every justified node is "in" and every node which is not justified is "out". ******************* THEOREM: The problem of determining the Doyle satisfiability of a set J of nm-justifications is NP-complete. ******************* PROOF: PSAT can be reduced to Doyle satisfiability as follows: Let C be any set of propositional clauses (i.e. a problem in PSAT). For each atomic proposition symbol P appearing in C let P and nP be two nodes and construct the following justifications: ({}, {nP}) => P (i.e. if nP is "out" then P is justified) ({}, {P}) => nP (i.e. if P is "out" then nP is justified) We introduce an additional node F (for "false") and for each clause (L1 or L2 ... or LN) in C we construct the justification: ({nL1, nL2, ... nLn} {F}) => F where the node nLj is the node nP if Lj is the symbol P and nLj is the node P if Lj is the literal (NOT P). The set J of nm-justifications constructed in this way is Doyle-Satisfiable iff the original set C is propositionally satisfiable. To verify this last claim note that if L is a labeling which satisfies J then exactly one of P and nP is "in"; if P is "out" then nP must be "in" and vice versa, and if P is "in" then nP must be "out" and vice versa. Next note that if L is a labeling satisfying J then F must be "out"; if F were "in" then no justification for F would be active contradicting the requirement that F is not justified then F is not "in". Finally note that a labeling L satisfies J just in case none of the justifications for F are active, i.e. just in case the corrosponding truth assignment to the proposition symbols in C satisfies every clause. ************** David McAllester ------------------------------ Date: 16 Oct 86 07:21:00 EDT From: "CUGINI, JOHN" Reply-to: "CUGINI, JOHN" Subject: Book alert This week's New Republic has a review of Thomas Nagel's (of What-is-it-like-to-be-a-bat fame) new book, "The View from Nowhere". For those interested in the philosophical issues associated with the objective/subjective distinction, it sounds like it's worth reading. John Cugini ------------------------------ Date: 15 Oct 86 23:17:57 GMT From: mnetor!utzoo!utcsri!utai!me@seismo.css.gov (Daniel Simon) Subject: Re: Searle, Turing, Symbols, Categories In article <167@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes: > >In response to my article <160@mind.UUCP>, Daniel R. Simon asks: > >> 1) To what extent is our discernment of intelligent behaviour >> context-dependent?...Might not the robot version [of the >> turing test] lead to the...problem of testers being >> insufficiently skeptical of a machine with human appearance? >> ...Is it ever possible to trust the results of any >> instance of the test...? > >My reply to these questions is quite explicit in the papers in >question: >The turing test has two components, (i) a formal, empirical one, >and (ii) an informal, intuitive one. The formal empirical component (i) >is the requirement that the system being tested be able to generate human >performance (be it robotic or linguistic). That's the nontrivial >burden that will occupy theorists for at least decades to come, as we >converge on (what I've called) the "total" turing test -- a model that >exhibits all of our robotic and lingistic capacities. By "nontrivial burden", do you mean the task of defining objective criteria by which to characterize "human performance"? If so, you are after the same thing as I am, but I fail to see what this has to do with the Turing test as originally conceived, which involved measuring up AI systems against observers' impressions, rather than against objective standards. Apparently, you're not really defending the Turing test at all, but rather something quite different. Moreover, you haven't said anything concrete about what this test might look like. On what foundation could such a set of defining characteristics for "human performance" be based? Would it define those attributes common to all human beings? Most human beings? At least one human being? How would we decide by what criteria to include observable attributes in our set of "human" ones? How could such attributes be described? Is such a set of descriptions even feasible? If not, doesn't it call into question the validity of seeking to model what cannot be objectively characterized? And if such a set of describable attributes is feasible, isn't it an indispensable prerequisite for the building of a working Turing-test-passing model? Please forgive my impertinent questions, but I haven't read your articles, and I'm not exactly clear about what this "total" Turing test entails. >The informal, >intuitive component (ii) is that the system in question must perform in a >way that is indistinguishable from the performance of a person, as >judged by a person. > >Now the only reply I have for the sceptic about (ii) is >that he should remember that he has nothing MORE than that to go on in >the case of any other mind than his own. In other words, there is no >rational reason for being more sceptical about robots' minds (if we >can't tell their performance apart from that of people) than about >(other) peoples' minds. The turing test is ALREADY the informal way we >contend with the "other-minds" problem [i.e., how can you be sure >anyone else but you has a mind, rather than merely acting AS IF it had >a mind?], so why should we demand more in the case of robots? ... > I'm afraid I must disagree. I believe that people in general dodge the "other minds" problem simply by accepting as a convention that human beings are by definition intelligent. For example, we use terms such as "autistic", "catatonic", and even "sleeping" to describe people whose behaviour would in most cases almost certainly be described as unintelligent if exhibited by a robot. Such people are never described as "unintelligent" in the sense of the word that we would use to describe a robot who showed the exact same behaviour patterns. Rather, we imply by using these terms that the people being described are human, and therefore *would* be behaving intelligently, but for (insert neurophysiological/psychological explanation here). This implicit axiomatic attribution of intelligence to humans helps us to avoid not only the "other minds" problem, but also the problem of assessing intelligence despite the effect of what I previously referred to loosely as the "context" of our observations. In short, we do not really use the Turing test on each other, because we are all well acquainted with how easily we can be fooled by contextual traps. Instead, we automatically associate intelligence with human beings, thereby making our intuitive judgment even less useful to the AI researcher working with computers or robots. >As to "context," as I argue in the paper, the only one that is >ultimately defensible is the "total" turing test, since there is no >evidence at all that either capacities or contexts are modular. The >degrees of freedom of a successful total-turing model are then reduced >to the usual underdetermination of scientific theory by data. (It's always >possible to carp at a physicist that his theoretic model of the >universe "is turing-indistinguishable from the real one, but how can >you be sure it's `really true' of the world?") > Wait a minute--You're back to component (i). What you seem to be saying is that the informal component (component (ii)) has no validity at all apart from the "context" of having passed component (i). The obvious conclusion is that component (ii) is superfluous; any system that passes the "total Turing test" exhibits "human behaviour", and hence must by definition be indistinguishable from a human to another human. >> 2) Assuming that some "neutral" context can be found... >> what does passing (or failing) the Turing test really mean? > >It means you've successfully modelled the objective observables under >investigation. No empirical science can offer more. And the only >"neutral" context is the total turing test (which, like all inductive >contexts, always has an open end, namely, the everpresent possibility >that things could turn out differently tomorrow -- philosophers call >this "inductive risk," and all empirical inquiry is vulnerable to it). > Again, you have all but admitted that the "total" Turing test you have described has nothing to do with the Turing test at all--it is a set of "objective observables" which can be verified through scientific examination. The thoughtful examiner and "comparison human" have been replaced with controlled scientific experiments and quantifiable results. What kinds of experiments? What kinds of results? WHAT DOES THE "TOTAL TURING TEST" LOOK LIKE? >> 3) ...are there more appropriate means by which we >> could evaluate the human-like or intelligent properties of an AI >> system? ...is it possible to formulate the qualities that >> constitute intelligence in a manner which is more intuitively >> satisfying than the standard AI stuff about reasoning, but still >> more rigorous than the Turing test? > >I don't think there's anything more rigorous than the total turing >test since, when formulated in the suitably generalized way I >describe, it can be seen to be identical to the empirical criterion for >all of the objective sciences... > >Stevan Harnad >princeton!mind!harnad One question you haven't addressed is the relationship between intelligence and "human performance". Are the two synonymous? If so, why bother to make artificial humans when making natural ones is so much easier (not to mention more fun)? And if not, how does your "total Turing test" relate to the discernment of intelligence, as opposed to human-like behaviour? I know, I know. I ask a lot of questions. Call me nosy. Daniel R. Simon "We gotta install database systems Custom software delivery We gotta move them accounting programs We gotta port them all to PC's...." ------------------------------ Date: 14 Oct 86 16:01:44 GMT From: ssc-vax!bcsaic!michaelm@beaver.cs.washington.edu Subject: Re: Searle, Turing, Symbols, Categories In article <167@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes: >...since there is no >evidence at all that either capacities or contexts are modular. Maybe I'm reading this out of context (not having read your books or papers), but could you explain this statement? I know of lots of evidence for the modularity of various aspects of linguistic behavior. In fact, we have a parser + grammar of English here that captures a large portion of English syntax, but has absolutely no semantics (yet). That is, it could parse Jabberwocky or your article (well, I can't quite claim that it would parse *all* of either one!) without having the least idea that your article is meaningful whereas Jabberwocky isn't (apart from an explanation by Humpty Dumpty). On the other hand, it wouldn't parse something like "book the table on see I", despite the fact that we might make sense of the latter (because of our world knowledge). Likewise, human aphasics often show similar deficits in one or another area of their speech or language understanding. If this isn't modular, what is? But as I say, maybe I don't understand what you mean by modular... -- Mike Maxwell Boeing Advanced Technology Center ...uw-beaver!uw-june!bcsaic!michaelm ------------------------------ Date: 16 Oct 86 06:17:51 GMT From: rutgers!princeton!mind!harnad@spam.ISTC.SRI.COM (Stevan Harnad) Subject: Re: Searle, Turing, Symbols, Categories In reply to a prior iteration D. Simon writes: > I fail to see what [your "Total Turing Test"] has to do with > the Turing test as originally conceived, which involved measuring > up AI systems against observers' impressions, rather than against > objective standards... Moreover, you haven't said anything concrete > about what this test might look like. How about this for a first approximation: We already know, roughly speaking, what human beings are able to "do" -- their total cognitive performance capacity: They can recognize, manipulate, sort, identify and describe the objects in their environment and they can respond and reply appropriately to descriptions. Get a robot to do that. When you think he can do everything you know people can do formally, see whether people can tell him apart from people informally. > I believe that people in general dodge the "other minds" problem > simply by accepting as a convention that human beings are by > definition intelligent. That's an artful dodge indeed. And do you think animals also accept such conventions about one another? Philosophers, at least, seem to have noticed that there's a bit of a problem there. Looking human certainly gives us the prima facie benefit of the doubt in many cases, but so far nature has spared us having to contend with any really artful imposters. Wait till the robots begin giving our lax informal turing-testing a run for its money. > What you seem to be saying is that [what you call] > the informal component [(i) of the turing test -- > i. e., indistinguishability from a person, as judged by a > person] has no validity at all apart from the "context" of > having passed [your] component (i) [i.e., the generation of > our total cognitive performance capacity]. The obvious > conclusion is that component (ii) is superfluous. It's no more superfluous than, say, the equivalent component in the design of an artificial music composer. First you get it to perform in accordance with what you believe to be the formal rules of (diatonic) composition. Then, when it successfully performs according to the rules, see whether people like its stuff. Peoples' judgments, after all, were not only the source of those rules in the first place, but without the informal aesthetic sense that guided them, the rules would amount to just that -- meaningless acoustic syntax. Perhaps another way of putting it is that I doubt that what guides our informal judgments (and underlies our capacities) can be completely formalized in advance. The road to Total-Turing Utopia will probably be a long series of feedback cycles between the formal and informal components of the test before we ever achieve our final passing grade. > One question you haven't addressed is the relationship between > intelligence and "human performance". Are the two synonymous? > If so, why bother to make artificial humans... And if not, how > does your "total Turing test" relate to the discernment of > intelligence, as opposed to human-like behaviour? Intelligence is what generates human performance. We make artificial humans to implement and test our theories about the substrate of human performance capacity. And there's no objective difference between human and (turing-indistinguishably) human-like. > WHAT DOES THE "TOTAL TURING TEST" LOOK LIKE?... Please > forgive my impertinent questions, but I haven't read your > articles, and I'm not exactly clear about what this "total" > Turing test entails. Try reading the articles. ****** I will close with an afterthought on "blind" vs. "nonblind" turing testing that I had after the last iteration: In the informal component of the total turing test it may be arguable that a sceptic would give a robot a better run for its money if he were pre-alerted to the possibility that it was a robot (i.e., if the test were conducted "nonblind" rather than "blind"). That way the robot wouldn't be inheriting so much of the a priori benefit of the doubt that had accrued from our lifetime of successful turing-testing of biological persons of similar appearance (in our everyday informal solutions to the "other-minds" problem). The blind/nonblind issue does not seem critical though, since obviously the turing test is an open-ended one (and probably also, like all other empirical conjectures, confirmable only as a matter of degree); so we probably wouldn't want to make up our minds too hastily in any case. I would say that several years of having lived amongst us, as in the sci-fi movies, without arousing any suspicions -- and eliciting only shocked incredulity from its close friends once the truth about its roots was revealed -- would count as a pretty good outcome on a "blind" total turing test. Stevan Harnad princeton!mind!harnad ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:13:18 1986 Date: Mon, 20 Oct 86 03:13:08 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe Subject: AIList Digest V4 #227 Status: R AIList Digest Sunday, 19 Oct 1986 Volume 4 : Issue 227 Today's Topics: Philosophy - Searle, Turing ---------------------------------------------------------------------- Date: 16 Oct 86 09:10:00 EDT From: "CUGINI, JOHN" Reply-to: "CUGINI, JOHN" Subject: yet more wrangling on Searle, Turing, ... > Date: 10 Oct 86 13:47:46 GMT > From: rutgers!princeton!mind!harnad@think.com (Stevan Harnad) > Subject: Re: Searle, Turing, Symbols, Categories > > It is not always clear which of the two components a sceptic is > worrying about. It's usually (ii), because who can quarrel with the > principle that a veridical model should have all of our performance > capacities? Now the only reply I have for the sceptic about (ii) is > that he should remember that he has nothing MORE than that to go on in > the case of any other mind than his own. In other words, there is no > rational reason for being more sceptical about robots' minds (if we > can't tell their performance apart from that of people) than about > (other) peoples' minds. This just ain't so... if we know, as we surely do, that the internals of the robot (electronics, metal) are quite different from those of other passersby (who presumably have regular ole brains), we might well be more skeptical that robots' "consciousness" is the same as ours. Briefly, I know: 1. that I have a brain 2. that I am conscious, and what my consciousness feels like 3. that I am capable of certain impressive types of performance, like holding up my end of an English conversation. It seems very reasonable to suppose that 3 depends on 2 depends on 1. But 1 and 3 are objectively ascertainable for others as well. So if a person has 1 and 3, and a robot has 3 but NOT 1, I certainly have more reason to believe that the person has 2, than that the robot does. One (rationally) believes other people are conscious BOTH because of their performance and because their internal stuff is a lot like one's own. I am assuming here that "mind" implies consciousness, ie that you are not simply defining "mind" as a set of external capabilities. If you are, then of course, by (poor) definition, only external performance is relevant. I would assert (and I think you would agree) that to state "X has a mind" is to imply that X is conscious. > ....So, since we have absolutely no intuitive idea about the functional > (symbolic, nonsymbolic, physical, causal) basis of the mind, our only > nonarbitrary basis for discriminating robots from people remains their > performance. Again, we DO have some idea about the functional basis for mind, namely that it depends on the brain (at least more than on the pancreas, say). This is not to contend that there might not be other bases, but for now ALL the minds we know of are brain-based, and it's just not dazzlingly clear whether this is an incidental fact or somewhat more deeply entrenched. > I don't think there's anything more rigorous than the total turing > test ... Residual doubts about it come from > four sources, ... (d) misplaced hold-outs for consciousness. > > Finally, my reply to (d) [mind bias] is that holding out for > consciousness is a red herring. Either our functional attempts to > model performance will indeed "capture" consciousness at some point, or > they won't. If we do capture it, the only ones that will ever know for > sure that we've succeeded are our robots. If we don't capture it, > then we're stuck with a second level of underdetermination -- call it > "subjective" underdetermination -- to add to our familiar objective > underdetermination (b)...[i.e.,] > there may be a further unresolvable uncertainty about whether or not > they capture the unobservable basis of everything (or anything) that is > subjectively observable. > > AI, robotics and cognitive modeling would do better to learn to live > with this uncertainty and put it in context, rather than holding out > for the un-do-able, while there's plenty of the do-able to be done. > > Stevan Harnad > princeton!mind!harnad I don't quite understand your reply. Why is consciousness a red herring just because it adds a level of uncertainty? 1. If we suppose, as you do, that consciousness is so slippery that we will never know more about its basis in humans than we do now, one might still want to register the fact that our basis for belief in the consciousness of competent robots is more shaky than for that in humans. This reservation does not preclude the writing of further Lisp programs. 2. But it's not obvious to me that we will never know more than we do now about the relation of brain to consciousness. Even though any correlations will ultimately be grounded on one side by introspection reports, it does not follow that we will never know, with reasonable assurance, which aspects of the brain are necessary for consciousness and which are incidental. A priori, no one knows whether, eg, being-composed-of-protein is incidental or not. I believe this is Searle's point when he says that the brain may be as necessary for consciousness as mammary glands are for lactation. Now at some level of difficulty and abstraction, you can always engineer anything with anything, ie make a computer out of play-doh. But the "multi- realizability" argument has force only if its obvious (which it ain't) that the structure of the brain at a fairly high level (eg neuron networks, rather than molecules), high enough to be duplicated by electronics, is what's important for consciousness. John Cugini ------------------------------ Date: 16 Oct 86 07:14:26 PDT (Thursday) From: "charles_kalish.EdServices"@Xerox.COM Subject: Turing Test(s?) Maybe we should start a new mail group where we try to convince each other that we understand the turing test if everybody fails we go back to the drawing board and design a new test. And as the first entry: In response to Daniel Simon's questioning of the appropriateness of this test, I think the answer is that the Turing test is acceptable because that's how we recognize each other as intelligent beings. Usually we don't do it in a rigorous way because everybody always passes it. But if I ask you to "please pass the Cheez-whiz" and you respond "Anita Eckbart is marinating her poodle" then I would get a little suspicious and ask more questions designed to figure out whether you're joking, sick, hard of hearing, etc. Depending on your answers I may decide to downgrade your status to less than full personhood. About Stevan Harnad's two kinds of Turing tests: I can't really see what difference the I/O methods of your system makes. It seems that the relevant issue is what kind of representation of the world it has. While I agree that to really understand the system would need some non-purely conventional representation (not semantic if "semantic" means "not operable on in a formal way" as I believe [given the brain is a physical system] all mental processes are formal then "semantic" just means governed by a process we don't understand yet) giving and getting through certain kinds of I/O doesn't make much difference. Two for instances: SHRDLU operated on a simulated blocks world. The modifications to make it operate on real block would have been peripheral and not have effected the understanding of the system. Also, all systems take analog input and give analog output. Most receive finger pressure on keys and return directed streams of ink or electrons. It may be that a robot would need more "immediate" (as opposed to conventional) representations, but it's neither necessary nor sufficient to be a robot to have those representations. P.s. don't ask me to be the moderator for this new group. The turing test always assumes the moderator has some claim to expertise in the matter. ------------------------------ Date: 16 Oct 86 17:11:04 GMT From: eugene@AMES-AURORA.ARPA (Eugene miya) Subject: Re: Turing, Symbols, Categories <2495@utai.UUCP> <2552@utai.UUCP> In article <2552@utai.UUCP>, me@utai.UUCP (Daniel Simon) writes: > In article <167@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes: > > > >The turing test has two components, (i) a formal, empirical one, > >and (ii) an informal, intuitive one. The formal empirical component (i) > >is the requirement that the system being tested be able to generate human > >performance (be it robotic or linguistic). That's the nontrivial > >burden that will occupy theorists for at least decades to come, as we > >converge on (what I've called) the "total" turing test -- a model that > >exhibits all of our robotic and lingistic capacities. > > Moreover, you haven't said anything concrete about what this test might look > like. On what foundation could such a set of defining characteristics for > "human performance" be based? Would it define those attributes common to all > human beings? Most human beings? At least one human being? How would we > decide by what criteria to include observable attributes in our set of "human" > ones? How could such attributes be described? Is such a set of descriptions > even feasible? If not, doesn't it call into question the validity of seeking > to model what cannot be objectively characterized? And if such a set of > describable attributes is feasible, isn't it an indispensable prerequisite for > the building of a working Turing-test-passing model? > > Again, you have all but admitted that the "total" Turing test you have > described has nothing to do with the Turing test at all--it is a set of > "objective observables" which can be verified through scientific examination. > The thoughtful examiner and "comparison human" have been replaced with > controlled scientific experiments and quantifiable results. What kinds of > experiments? What kinds of results? WHAT DOES THE "TOTAL TURING TEST" > LOOK LIKE? > > I know, I know. I ask a lot of questions. Call me nosy. > > Daniel R. Simon Keep asking questions. 1) I deleted your final comment about database: note EXPERT SYSTEMS (so called KNOWLEDGE-BASED SYSTEMS) ARE NOT AI. 2) I've been giving thought to what a `true' Turing test would be like. I found Turing's original paper in Mind. This is what I have concluded with light thinking for about 8 months: a) No single question can answer the question of intelligence, then how many? I hope a finite, preferably small, or at least a countable number. b) The Turing test is what psychologists call a test of `Discrimination.' These tests should be carefully thought out for pre-test and post-test experimental conditions (like answers of a current question may or may not be based on answers from an earlier [not necessarily immediate question]). c) Some of the questions will be confusing, sort of like the more sophisticated eye tests like I just had. Note we introduce the possibly of calling some human "machines." d) Early questions in the tests in particular those of quantitative reasoning should be timed as well as checked for accuracy. Turing would want this. to was in his original paper. e) The test must be prepared for ignorance on the part of humans and machines. It should not simply take "I don't know," or "Not my taste" for answers. It should be able to circle in on one's ignorance to define the boundaries or character of the respondent's ignorance. f) Turing would want a degree of humor. The humor would be a more sophisticated type like punning or double entandres. Turing would certainly consider gaming problems. Turing mentions all these in his paper. Note that some of the original qualities make AI uneconomical in the short term. Who wants a computer which makes adding errors? Especially it's it dealing with my pay check. I add that a) We should check for `personal values,' `compassion,' which might traits or artifacts of the person or team responsible for programming. It should exploit those areas as possible lines of weakness or strenth. b) The test should have a degree of dynamic problem solving. c) The test might have characteristics like that test in the film Blade Runner. Note: memories != intelligence, but the question might be posed to the respondent in such a way: "Your wife and your daughter have fallen into the water. You can only save one. Who do you save? and why?" d) Consider looking at the WAIS, WISC, the Stanford-Binet, the MMPI (currently being updated), the Peabody, and numerous other tests of intelligence and personality, etc. Note there are tests which distinguish split brain people. They are simple tests. Consider the color-blindness tests: simple if you are not color blind, confusing is you are. There is a whole body of psychometric literature which Turing did not consult. As you can guess, such a test cannot be easily placed as a sequence on paper, but as a program in a dumb machine, it is certainly possible. As a last thought. The paper in Mind was published in 1950. Turing made comment about "computers with the capacity of a billion [what he did not say]," and the "turn of the Century." I suggested to Doug Hofstadter (visiting here one day), we hold a 50th anniversary celebration in the year 2000 on the publication of Turing paper, and he agreed. >From the Rock of Ages Home for Retired Hackers: --eugene miya NASA Ames Research Center eugene@ames-aurora.ARPA "You trust the `reply' command with all those different mailers out there?" {hplabs,hao,nike,ihnp4,decwrl,allegra,tektronix,menlo70}!ames!aurora!eugene I need a turing machine to route my mail. ------------------------------ End of AIList Digest ******************** From csnet_gateway Mon Oct 20 03:13:33 1986 Date: Mon, 20 Oct 86 03:13:26 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe Subject: AIList Digest V4 #228 Status: R AIList Digest Sunday, 19 Oct 1986 Volume 4 : Issue 228 Today's Topics: Philosophy - Searle, Turing, Symbols, Categories ---------------------------------------------------------------------- Date: 16 Oct 86 17:25:42 GMT From: rutgers!princeton!mind!harnad@lll-crg.arpa (Stevan Harnad) Subject: Re: Searle, Turing, Symbols, Categories In reply to the following by me in <167@mind.UUCP>: > there is no evidence at all that > either capacities or contexts are modular. michaelm@bcsaic.UUCP (michael maxwell) writes: >> Maybe I'm reading this out of context (not having read your books or papers), >> but could you explain this statement? I know of lots of evidence for the >> modularity of various aspects of linguistic behavior. In fact, we have a >> parser + grammar of English here that captures a large portion of English >> syntax, but has absolutely no semantics (yet). I'm afraid this extract is indeed a bit out of context. The original context concerned what I've dubbed the "Total Turing Test," one in which ALL of our performance capacities -- robotic and linguistic -- are "captured." In the papers under discussion I described several arguments in favor of the Total Turing Test over any partial turing test, such as "toy" models that only simulate a small chunk of our cognitive performance capacity, or even the (subtotal) linguistic ("teleteype") version of the Total Turing Test. These arguments included: (3) The "Convergence Argument" that `toy' problems are arbitrary, that they have too many degrees of freedom, that the d.f. shrink as the capacities of the toy grow to life-size, and that the only version that reduces the underdetermination to the normal proportions of a scientific theory is the `Total' one. (5) The "Nonmodularity Argument" that no subtotal model constitutes a natural module (insofar as the turing test is concerned); the only natural autonomous modules are other organisms, with their complete robotic capacities (more of this below). (7) The "Robotic Functionalist Argument" that the entire symbolic functional level is no macromodule either, and needs to be grounded in robotic function. I happen to have views on the "autonomy of syntax" (which is of course the grand-daddy of the current modulo-mania), but they're not really pertinent to the total vs modular turing-test issue. Perhaps the only point about an autonomous parser that is relevant here is that it is in the nature of the informal, intuitive component of the turing test that lifeless fragments of mimicry (such as Searle's isolated `thirst' module) are not viable; they simply fail to convince us of anything. And rightly so, I should think; otherwise the turing test would be a pretty flimsy one. Let me add, though, that even "convincing" autonomous parsing performance (in the non-turing sense of convincing) seems to me to be rather weak evidence for the psychological reality of a syntactic module -- let alone that it has a mind. (On my theory, semantic performance has to be grounded in robotic performance and syntactic performance must in turn be grounded in semantic performance.) Stevan Harnad (princeton!mind!harnad) ------------------------------ Date: Thu 16 Oct 86 17:55:00-PDT From: Pat Hayes Subject: symbols Stevan Harnad has answered Drew Lawson nicely, but I cant help adding this thought: if he saw a symbol of a car coming and DIDNT get out of the way, would the resulting change of his state be a purely symbolic one? Pat Hayes ------------------------------ Date: 17 Oct 1986 1329-EDT From: Bruce Krulwich Subject: symbols: syntax vs semantics i think that the main thing i disagree with about Searle's work and recent points in this discussion is the claim that symbols, and in general any entity that a computer will process, can only be dealt with in terms of syntax. i disagree. for example, when i add two integers, the bits that the integers are encoded in are interpreted semantically to combine to form an integer. the same could be said about a symbol that i pass to a routine in an object-oriented system such as CLU, where what is done with the symbol depends on it's type (which i claim is it's semantics) i think that the reason that computers are so far behind the human brain in semantic interpretation and in general "thinking" is that the brain contains a hell of a lot more information than most computer systems, and also the brain makes associations much faster, so an object (ie, a thought) is associated with its semantics almost instantly. bruce krulwich arpa: krulwich@c.cs.cmu.edu bitnet: bk0a%tc.cc.cmu.edu@cmuccvma uucp: (??) ... uw-beaver!krulwich@c.cs.cmu.edu or ... ucbvax!krulwich@c.cs.cmu.edu "Life's too short to ponder garbage" ------------------------------ Date: Fri 17 Oct 86 10:04:51-PDT From: Pat Hayes Subject: turing test Daniel R. Simon has worries about the Turing test. A good place to find intelligent discussion of these issues is Turings original article in MIND, October 1950, v.59, pages 433 to 460. Pat Hayes PHAYES@SRI-KL ------------------------------ Date: 14 Oct 86 21:20:53 GMT From: adelie!axiom!linus!philabs!pwa-b!mmintl!franka@ll-xn.arpa (Frank Adams) Subject: Re: Searle, Turing, Symbols, Categories In article <166@mind.UUCP> harnad@mind.UUCP writes: >What I mean by a symbol is an >arbitrary formal token, physically instantiated in some way (e.g., as >a mark on a piece of paper or the state of a 0/1 circuit in a >machine) and manipulated according to certain formal rules. The >critical thing is that the rules are syntactic, that is, the symbol is >manipulated on the basis of its shape only -- which is arbitrary, >apart from the role it plays in the formal conventions of the syntax >in question. The symbol is not manipulated in virtue of its "meaning." >Its meaning is simply an interpretation we attach to the formal >goings-on. Nor is it manipulated in virtue of a relation of >resemblance to whatever "objects" it may stand for in the outside >world, or in virtue of any causal connection with them. Those >relations are likewise mediated only by our interpretations. I see two problems with respect to this viewpoint. One is that relating purely symbolic functions to external events is essentially a solved problem. Digital audio recording, for example, works quite well. Robotic operations generally fail, when they do, not because of any problems with the digital control of an analog process, but because the purely symbolic portion of the process is inadequate. In other words, there is every reason to expect that a computer program able to pass the Turing test could be extended to one able to pass the robotic version of the Turing test, requiring additional development effort which is tiny by comparison (though likely still measured in man-years). Secondly, even in a purely formal environment, there turn out to be a lot of real things to talk about. Primitive concepts of time (before and after) are understandable. One can talk about nouns and verbs, sentences and conversations, self and other. I don't see any fundamental difference between the ability to deal with symbols as real objects, and the ability to deal with other kinds of real objects. Frank Adams ihnp4!philabs!pwa-b!mmintl!franka Multimate International 52 Oakland Ave North E. Hartford, CT 06108 ------------------------------ Date: 17 Oct 86 19:35:51 GMT From: adobe!greid@glacier.stanford.edu Subject: Re: Searle, Turing, Symbols, Categories It seems to me that the idea of concocting a universal Turing test is sort of useless. Consider, for a moment, monsters. There have been countless monsters on TV and film that have had varying degrees of human-ness, and as we watch the plot progress, we are sort of administering the Turing test. Some of the better aliens, like in "Blade Runner", are very difficult to detect as being non-human. However, given enough time, we will eventually notice that they don't sleep, or that they drink motor oil, or that they don't bleed when they are cut (think of "Terminator" and surgery for a minute), and we start to think of alternative explanations for the aberrances we have noticed. If we are watching TV, we figure it is a monster. If we are walking down the street and we see somebody get their arm cut off and they don't bleed, we think *we* are crazy (or we suspect "special effects" and start looking for the movie camera), because there is no other plausible explanation. There are even human beings whom we question when one of our subconscious "tests" fails--like language barriers, brain damage, etc. If you think about it, there are lots of human beings who would not pass the Turing test. Let's forget about it. Glenn Reid Adobe Systems Adobe claims no knowledge of anything in this message. ------------------------------ Date: 18 Oct 86 15:16:14 GMT From: rutgers!princeton!mind!harnad@titan.arc.nasa.gov (Stevan Harnad) Subject: Re: Searle, Turing, Symbols, Categories In response to some of the arguments in favor of the robotic over the symbolic version of the turing test in (the summaries of) my articles "Minds, Machines and Searle" and "Category Induction and Representation" franka@mmintl.UUCP (Frank Adams) replies: > [R]elating purely symbolic functions to external events is > essentially a solved problem. Digital audio recording, for > example, works quite well. Robotic operations generally fail, > when they do, not because of any problems with the digital > control of an analog process, but because the purely symbolic > portion of the process is inadequate. In other words, there is > every reason to expect that a computer program able to pass the > [linguistic version of the] Turing test could be extended to one > able to pass the robotic version...requiring additional development > effort which is tiny by comparison (though likely still measured > in man-years). This argument has become quite familiar to me from delivering the oral version of the papers under discussion. It is the "Triviality of Transduction [A/D conversion, D/A conversion, Effectors] Argument" (TT for short). Among my replies to TT the central one is the principled Antimodularity Argument: There are reasons to believe that the neat partitioning of function into autonomous symbolic and nonsymbolic modules may break down in the special case of mind modeling. These reasons include my "Groundedness" Argument: that unless cognitive symbols are grounded (psychophysically, bottom-up) in nonsymbolic processes they remain meaningless. (This amounts to saying that we must be intrinsically "dedicated" devices and that our A/D and our "decryption/encryptions" are nontrivial; in passing, this is also a reply to Searle's worries about "intrinsic" versus "derived" intentionality. It may also be the real reason why "the purely symbolic portion of the process is inadequate"!) This problem of grounding symbolic processes in nonsymbolic ones in the special case of cognition is also the motivation for the material on category representation. Apart from nonmodularity and groundedness, other reasons include: (1) Searle's argument itself, and the fact that only the transduction argument can block it; that's some prima facie ground for believing that the TT may be false in the special case of mind-modeling. (2) The triviality of ordinary (nonbiological) transduction and its capabilities, comparared to what organisms with senses (and minds) can do. (Compare the I/O capacities of "audio" devices with those of "auditory" ones; the nonmodular road to the capacity to pass the total turing test suggests that we are talking here about qualitative differences, not quantitative ones.) (3) Induction (both ontogenetic and phylogentetic) and inductive capacity play an intrinsic and nontrivial role in bio-transduction that they do not play in ordinary engineering peripherals, or the kinds of I/O problems these have been designed for. (4) Related to the Simulation/Implementation Argument: There are always more real-world contingencies than can be anticipated in a symbolic description or simulation. That's why category representations are approximate and the turing test is open-ended. For all these reasons, I believe that Object/Symbol conversion in cognition is a considerably more profound problem than ordinary A/D; orders of magnitude more profound, in fact, and hence that TT is false. > [E]ven in a purely formal environment, there turn out to be a > lot of real things to talk about. Primitive concepts of time > (before and after) are understandable. One can talk about nouns > and verbs, sentences and conversations, self and other. I don't > see any fundamental difference between the ability to deal with > symbols as real objects, and the ability to deal with other kinds > of real objects. I don't completely understand the assumptions being made here. (What is a "purely formal environment"? Does anyone you know live in one?) Filling in with some educated guesses here, I would say that again the Object/Symbol conversion problem in the special case of organisms' mental capacities is being vastly underestimated. Object-manipulation (including discrimination, categorization, identification and description) is not a mere special case of symbol-manipulation or vice-versa. One must be grounded in the other in a principled way, and the principles are not yet known. On another interpretation, perhaps you are talking about "deixis" -- the necessity, even in the linguistic (symbolic) version of the turing test, to be able to refer to real objects in the here-and-now. I agree that this is a deep problem, and conjecture that its solution in the symbolic version will have to draw on anterior nonsymbolic (i.e., robotic) capacities. Stevan Harnad princeton!mind!harnad ------------------------------ End of AIList Digest ******************** From csnet_gateway Sat Oct 25 02:03:39 1986 Date: Sat, 25 Oct 86 02:03:30 edt From: csnet_gateway (LAWS@SRI-STRIPE.ARPA) To: ailist@sri-stripe.arpa Subject: AIList Digest V4 #229 Status: R AIList Digest Thursday, 23 Oct 1986 Volume 4 : Issue 229 Today's Topics: Seminars - Toward a Learning Robot (CMU) & Implementing Scheme on a Personal Computer (SMU) & Functional Representations in Knowledge Programming (UTexas) & The Hypotheses Underlying Connectionism (UCB) & Advances in Computational Robotics (CMU) & More Agents are Better Than One (SU) & Automatic Schematics Drafting (CMU) & Learning by Failing to Explain (MIT) ---------------------------------------------------------------------- Date: 15 October 1986 1505-EDT From: Elaine Atkinson@A.CS.CMU.EDU Subject: Seminar - Toward a Learning Robot (CMU) SPEAKER: Tom Mitchell, CMU, CS Department TITLE: "Toward a Learning Robot" DATE: Thursday, October 16 TIME: 4:00 p.m. PLACE: Adamson Wing, Baker Hall ABSTRACT: Consider the problem of constructing a learning robot; that is, a system that interfaces to some environment via a set of sensors and effectors, and which builds up a theory of its environment in order to control the environment in accordance with its goals. One instantiation of this problem is to construct a hand-eye system that can learn to manipulate a collection of blocks and to build simple structures from these blocks. We are starting a new research project to develop such a learning robot, and this talk will present some preliminary ideas about how to proceed. The talk will consider a number of questions such as what general cognitive architecture seems reasonable? What kinds of knowledge must such a robot learn? How should this knowledge be represented? How will it learn? How can the robot solve problems with only an incomplete understanding of its world? Can it use sensory feedback to make up for ambiguity in its world theory? There will probably be more questions than answers, so please bring your own. ------------------------------ Date: WED, 20 apr 86 17:02:23 CDT From: leff%smu@csnet-relay Subject: Seminar - Implementing Scheme on a Personal Computer (SMU) Implementing Scheme on a Personal Computer Speaker: David Bartley Location: 315 SIC Texas Instruments Time: 2:00 PM PC Scheme is an implementation of the Scheme language, a lexically scoped, applicative order, and properly tail-recursive dialect of LISP. PC Scheme was implemented for IBM and TI personal computers within the Symbolic Computing Laboratory at Texas Instruments. The presentation will examine some of the pragmatic aspects of developing a production-quality LISP system for small machines. These include: compilation vs. interpretation, using a byte-threaded virtual machine for compact code, the architecture of the virtual machine, runtime representation issues, compiler design, debugging issues, and performance. Some significant differences between LISP and conventional language implementations will be highlighted. ------------------------------ Date: Fri 17 Oct 86 12:56:46-CDT From: Ellie Huck Subject: Seminar - Functional Representations in Knowledge Programming (UTexas) Please join the AI Group for the following talk October 22 at 11:00am in the Balcones 4th Floor Conference Room 4.302: KNOWLEDGE PROGRAMMING USING FUNCTIONAL REPRESENTATIONS Peter E. Hart Syntelligence SYNTEL is a novel knowledge representation language that provides traditional features of expert system shells within a pure functional programming paradigm. However, it differs sharply from existing functional languages in many ways, ranging from its ability to deal with uncertainty to its evaluation procedures. A very flexible user-interface facility, tightly integrated with the SYNTEL interpreter, gives the knowledge engineer full control over both form and content of the end-user system. SYNTEL executes in both LISP machine and IBM mainframe/workstation environments, and has been used to develop large knowledge bases dealing with the assessment of financial risks. This talk will present an overview of its architecture, as well as describe the real-world problems that motivated its development. October 22, 1986 11:00am Balcones Room 4.302 ------------------------------ Date: Mon, 20 Oct 86 10:30:58 PDT From: admin%cogsci.Berkeley.EDU@berkeley.edu (Cognitive Science Program) Subject: Seminar - The Hypotheses Underlying Connectionism (UCB) BERKELEY COGNITIVE SCIENCE PROGRAM Cognitive Science Seminar - IDS 237A Tuesday, October 28, 11:00 - 12:30* 2515 Tolman Hall Discussion: 12:30 - 1:30 2515 Tolman Hall ``The Hypotheses Underlying Connectionism'' Paul Smolensky Department of Computer Science & Institute of Cognitive Science University of Colorado at Boulder Cognitive models using massively parallel, nonsymbolic computation have now been developed for a considerable variety of cognitive processes. What are the essential hypotheses underlying these connectionist models? A satisfactory formulation of these hy- potheses must handle a number of attacks: -Nothing really new can be offered since Turing machines are universal -Connectionism just offers implementation details -Conscious, rule-guided behavior is ignored -The wrong kind of explanations are given for behavior -The models are too neurally unfaithful -Logic, rationality, and the structure of mental states are ignored -Useful AI concepts like frames and productions are ignored. Firstly, an introduction to connectionist models which describes the kind of computation they use will be presented and secondly, a general connectionist approach that faces the challenges listed above will be introduced. ------------------------------ Date: 20 October 1986 1309-EDT From: Richard Wallstein@A.CS.CMU.EDU Subject: Seminar - Advances in Computational Robotics (CMU) Robotics Seminar, FRIDAY Oct. 24, 2 PM, 4623 WeH John H. Reif Computer Science Department Duke University ADVANCES IN THE THEORY OF COMPUTATIONAL ROBOTICS This talk surveys work on the computational complexity of various movement planning problems relevant to robotics. The generalized mover's problem is to plan a sequence of movements of linked polyhedra through 3-dimensional Euclidean space, avoiding contact with a fixed set of polyhedra obstacles. We discuss algorithms for solving restricted mover's problems and our proof that generalized mover's problems are polynominal space hard. We also discuss our results on the computational complexity (both algorithms and lower bounds) of three other quite different types of movement problems; 1. movement planning in the presence of friction; 2. minimal movement planning; 3. dynamic movement planning with moving obstacles. ------------------------------ Date: 20 Oct 86 1141 PDT From: Vladimir Lifschitz Subject: Seminar - More Agents are Better Than One (SU) MORE AGENTS ARE BETTER THAN ONE Michael Georgeff Artificial Intelligence Center SRI International Thursday, October 23, 4pm MJH 252 A recent paper by Steve Hanks and Drew Mcdermott shows how some previous "solutions" to the frame problem turn out to be inadequate, despite appearances otherwise. They use a simple example -- come to be called the "Yale Shooting Problem" -- for which it is impossible to derive some expected results -- in this case, that the target of a shooting event ceases living. Such difficulties, they suggest, call into question the utility of nonmonotonic logics for solving the frame problem. In this talk, we describe a theory of action suited to multiagent domains, and show how this formulation avoids the problems raised by Hanks and McDermott. In particular, we show how the Yale Shooting Problem can be solved using a generalized form of the situation calculus for multiagent domains, together with notions of causality and independence. The solution does not rely on complex generalizations of nonmonotonic logics or circumscription, but instead uses traditional circumscription. We will also argue that most problems traditionally viewed as involving a single agent are better formulated as multiagent problems, and that the frame problem, as usually posed, is not what we should be attempting to solve. ------------------------------ Date: 20 Oct 86 15:23:35 EDT From: Steven.Minton@k.cs.cmu.edu Subject: Seminar - Automatic Schematics Drafting (CMU) This week's seminar is being led by Raul Valdes-Perez. Friday, 3:15 in 7220. Be there. Here's the abstract: Title: "Automatic Schematics Drafting: Aesthetic Configuration as a Design Task". To draft a schematic means to depict (say on paper) the electrical connections and function of a circuit. Aspects of this work are the following: 1. A design task that uses other than a production-system architecture. 2. An approach to "space planning" that is modern in the sense of exploiting dependency-directed backtracking and constraint-posting. 3. The idea of contradicton-fixing rules that exploit the richness of information when an inconsistency occurs. 4. Study of a linear-inequality-based representation of partial task solutions, and the properties of this representation. 5. A backtracking scheme suited to the search regimen used. ------------------------------ Date: Tue, 21 Oct 1986 12:05 EDT From: JHC%OZ.AI.MIT.EDU@XX.LCS.MIT.EDU Subject: Seminar - Learning by Failing to Explain (MIT) LEARNING BY FAILING TO EXPLAIN Robert Joseph Hall MIT Artificial Intelligence Laboratory Explanation-based Generalization depends on having an explanation on which to base generalization. Thus, a system with an incomplete or intractable explanatory mechanism will not be able to generalize some examples. It is not necessary, in those cases, to give up and resort to purely empirical generalization methods, because the system may already know almost everything it needs to explain the precedent. Learning by Failing to Explain is a method which exploits current knowledge to prune complex precedents and rules, isolating their mysterious parts. This paper describes two techniques for Learning by Failing to Explain: Precedent Analysis, partial analysis of a precedent or rule to isolate the mysterious new technique(s) it embodies; and Rule Re-analysis, re-analyzing old rules in terms of new rules to obtain a more general set. Thursday, October 23, 4pm NE-43, 8th floor playroom ------------------------------ End of AIList Digest ********************