Date: Thu 16 Jun 1988 23:42-EDT From: AIList Moderator Nick Papadakis Reply-To: AIList@AI.AI.MIT.EDU Us-Mail: MIT Mail Stop 38-390, Cambridge MA 02139 Phone: (617) 253-2737 Subject: AIList Digest V7 #36 To: AIList@AI.AI.MIT.EDU Status: RO AIList Digest Friday, 17 Jun 1988 Volume 7 : Issue 36 Today's Topics: Philosophy: Me and Karl Kluge The Social Construction of Reality Cognitive AI vs Expert Systems Dance notation definition of information Announcements: object-oriented database workshop: oopsla '88 LP'88 Conference Announcement ---------------------------------------------------------------------- Date: 10 Jun 88 14:42:15 GMT From: mcvax!ukc!strath-cs!glasgow!gilbert@uunet.uu.net (Gilbert Cockton) Subject: Re: Me and Karl Kluge (no flames, no insults, no abuse) In article <43@aipna.ed.ac.uk> Richard Caley writes: >In <1312@crete.cs.glasgow.ac.uk>, Gilbert Cockton writes >> work other than SOAR and ACT* where the Task Domain has been formally >> studied before the computer implementation? >Natural language processing. Much ( by no means all ) builds on the work >of some school of linguistics. and ignores most of the work beyond syntax :-) Stick to the computable, not the imponderable. Hmm pragmatics. I know there is AI work on pragmtics, but I don't know if a non-computational linguist working on semantics and pragmatics would call it advanced research work. >One stands on the shoulders of giants. Nobody has time to research >their subject from the ground up. But what when there is no ground? Then what? Hack first or study? Take intelligent user interfaces, hacking first well before any study of what problems real users on real tasks in real applications face (exception Interllisp-D interface, but this was an end-user project!). >According to your earlier postings, if ( strong ) AI was successful it >would tell us that we have no free will, or at least that we can not assume >we have it. I don't agree with this but it is _your_ argument and something >which a computer program could tell us. Agreed. Anything ELSE though that may be useful? (I accept that proof of our logical (worse than physical) determinism would be a revelation) >What do the theories of physics tell us that we couldn't find out by >studying objects. Nothing, but as these theories are based on the study of objects, we know that if we were to repeat the study, we would confirm the theories. Strong AI on the other hand conducts NO study of people, and thus if we studied people in an area colonised at present by hackers only, then we have no reason to believe that we would confirm the model in the hacker's mind. There is no similarity at all between the theories of physics and computational models of human behaviour, it just so happens that some (like ACT*) do have an empirical input. The problem with strong AI is that you don't have to have this input. No one would dare call something a theory in physics which was based solely on one individual's introspection constrained only by their progamming ability. In AI, it seems acceptable (Schank's work for example, can anyone give me references to the substantive critiques from within AI, I know of ones by linguists). >> The proper object of the study of humanity is humans, not machines >Well, there go all the physical sciences, botany, music, mathematics . . . And there goes your parser too. "of humanity" attaches to "the study". Your list is not such a study, it is a study of the natural world and some human artifacts (music, mathematics). These are not studies of people, OK, and they thus tell us nothing essential about ourselves, except that we can make music and maths, and that we can find structures and properties for these artifacts. A study of artifacts, cognitive, aesthetic or otherwise, is not necessarily a study of humanity. The latter will embrace all artifacts, but not as objects in themselves, but for their possible meanings. -- Gilbert Cockton, Department of Computing Science, The University, Glasgow gilbert@uk.ac.glasgow.cs !ukc!glasgow!gilbert The proper object of the study of humanity is humans, not machines ------------------------------ Date: 13 Jun 88 15:14:53 GMT From: mcvax!ukc!its63b!aipna!rjc@uunet.uu.net (Richard Caley) Subject: Re: Me and Karl Kluge In <1342@crete.cs.glasgow.ac.uk> Gilbert Cockton writes: >In article <43@aipna.ed.ac.uk> Richard Caley writes: >>Natural language processing...builds on the work of linguistics. >and ignores most of the work beyond syntax :-) Some does. >Hmm pragmatics. I know there is AI >work on pragmtics, but I don't know if a non-computational linguist >working on semantics and pragmatics would call it advanced research work. The criterion for it being interesting would not necessarily be explaining something new, explaining something in a more extensible/elegant/practical/ formal ( choose your own hobby horse ) is just as good. >But what when there is no ground? Then what? Hack first or study? Maybe my metaphor was not well chosen. Rather than building up it might be better to see the computational work building down, trying to ground its borrowed theories ( of language or whatever ) in something other than their own symbols and/or set theory. your question becomes, what when there is nothing to hang your work from? In that case you should go out and do the groundwork or, better, get someone trained in the empirical methods of that field to do it. >(exception Interllisp-D interface, but this was an end-user project!). ARGH don't even mention it, it just lost my days work for me. >(I accept that proof of our logical (worse than physical) determinism >would be a revelation) Well physical determinism wouldn't be a revelation to many of us who assume it already. I don't know your definition of logical determinism so I can't say whether that is worse. If it is meant to apply to all possible outcomes of strong AI it can't imply lack of free will ( read as the property of making your own decisions, rather than exclusion from causality ), what does it imply that is so shocking. >>What do the theories of physics tell us that we couldn't find out by >>studying objects. >Nothing. But they do. Studying objects just tells you what has happened. A (correct) theory can be predictive, can be explainatory, can allow one to deduce properties of the system under study which are not derivable from the data. >Strong AI on the other hand conducts NO study of people, Strong AI does not require the study of people, it is not "computational psycology". AI workers study people in order to avoid reinventing wheels. >>> The proper object of the study of humanity is humans, not machines >And there goes your parser too. Oops. I'm afraid I read it as parallel to "The proper study of man is man". It does seem to be something of a hollow statement; I can't think of many people who study machines as a study of humanity ( except in the degenerate case, if one believes humans are machines ). Some people use machines as tools to study human beings, some study and build machines. ------------------------------ Date: 15 Jun 88 15:39:28 GMT From: amdahl!pyramid!prls!philabs!gcm!dc@ames.arpa (Dave Caswell) Subject: Re: The Social Construction of Reality In article <514@dcl-csvax.comp.lancs.ac.uk> Simon Brooke writes [. . .]: .Wells, like fanatical adherents of other ideologies before him, first .hurls abuse at his opponents, and finally, defeated, closes his ears. I .note that he is in industry and not an academic; nevertheless he is .posting into the ai news group, and must therefore be considered part of .the American AI community. I haven't visited the States; I wonder if .someone could tell me whether this extraordinary combination of ignorance .and arrogance is frequently encountered in American intellectual life? Yes it is extremely common, and not just within the AI community. -- Dave Caswell Greenwich Capital Markets uunet!philabs!gcm!dc If it could mean something, I wouldn't have posted about it! -- Brian Case ------------------------------ Date: 17 Jun 88 01:17:03 GMT From: krulwich-bruce@yale-zoo.arpa (Bruce Krulwich) Subject: Cognitive AI vs Expert Systems (was Re: Me, Karl, Stephen, Gilbert) In article <19880615061536.5.NICK@INTERLAKEN.LCS.MIT.EDU> dg1v+@ANDREW.CMU.EDU (David Greene) writes: >I'm not advocating Mr. Cockton's views, but the limited literature breadth in >many AI papers *is* self-defeating. For example, until very recently, few >expert system papers acknowledged the results of 20+ years of psychology >research on Judgement and Decision Making. This says something about expert systems papers, not about papers discussing serious attempts at modelling intelligence. It is wrong to assume (as both you and Mr. Cockton are) that the expert system work typical of the business world (in other words, applications programs) is at all similar to work done by researchers investigating serious intelligence. (See work on case based reasoning, explanation based learning, expectation based processing, plan transformation, and constraint based reasoning, to name a few areas.) Bruce Krulwich Net-mail: krulwich@{yale.arpa, cs.yale.edu, yalecs.bitnet, yale.UUCP} Goal in life: to sit on a quiet beach solving math problems for a quarter and soaking in the rays. ------------------------------ Date: 13 Jun 88 08:44:22 GMT From: mcvax!ukc!strath-cs!glasgow!gilbert@uunet.uu.net (Gilbert Cockton) Subject: Re: Human-human communication In article <905@papaya.bbn.com> Hunter Barr writes: >But you ignore the existance of useful dance notations. I don't know >much about dance notation, and I am sure there is much lacking in it-- For an accessible introduction to the problem of dance notations, see Singh, Beatty, Booth and Ryman in Siggraph'83. You can chase up references from here. All I can add is that many choreographers (All I have encountered) do NOT use notations, as none are up to the job. There's research at New York into using figure animation, computer graphics and body sensors (Columbia I think). -- Gilbert Cockton, Department of Computing Science, The University, Glasgow gilbert@uk.ac.glasgow.cs !ukc!glasgow!gilbert The proper object of the study of humanity is humans, not machines ------------------------------ Date: Wed, 15 Jun 88 23:06:13 PDT From: John B. Nagle Subject: Re: Dance notation Smoliar's comment that no dance notation provides sufficient information for the exact reproduction of a movement is true as far as it goes, but misleading. Modern dance notation, by which I mean Labanotation or, as it is sometimes called, kinetography Laban, is designed to convey, in a concise form, the constraints on a movement necessary to produce the desired effect. Although the notation provides for detailed description of arm and hand motions, for example, the choreographer will not ordinarily specify these unless they are essential to the movement desired. Movements not specified are left to the discretion of the dancer. Placing the dancer under unnecessarily tight constraints will result in an unnaturally stiff performance (it is an ideal in ballet to achieve fluidity despite overconstraint by the choreographer, but the ideal is reached only in the better professional companies and at high cost to both company and dancers). Nor is it usually necessary. Thus the tendency to specify only the necessary. The discretion of the dancer in executing a movement specified only in outline, or what is referred to as "motif writing" in Labanotation, is not unlimited. There are defaults. Where forward motion is specified without additional annotation, a normal walk is assumed. There are sufficient conventions to produce a generally similar performance should two dancers perform from the same notation. As a technical tour de force, it is quite possible, by the way, to record in great detail the positions of the human body during a dance. Both the inventor of VPL's "Z-glove" and the MIT Media Lab have developed systems for so doing. It is not at all clear, though, what one does with the information so obtained. One can play it back through an animation system, of course. But it is not likely to be useful to a dancer. John Nagle ------------------------------ Date: Thu, 16 Jun 88 10:49:16 PDT From: Bob Riemenschneider Subject: Re^2: definition of information => From: bnevin@CCH.BBN.COM (Bruce E. Nevin) => => My understanding is that Carnap and Bar-Hillel set out to establish a => "calculus of information" but did not succeed in doing so. I'm not sure what your criteria for success are, but it looks pretty good to me. They didn't completely solve the problem of laying a foundation for Carnap's approach to inductive logic. (But it certainly advanced the state of the art--see, e.g., the second of Carnap's _Two Essays on Entropy_, which was, obviously, heavily influenced by this work.) Advances have been made since the original paper as well: see the bibiographies for Hintikka's paper and Carnap's later works on inductive logic (especially "A System of Inductive Logic" in _Studies in Inductive Logic_, vols. 1 and 2). [Disclaimer: There are very serious problem's with Carnap's approach to induction, which I have no wish to defend.] => Communication theory refers to a physical system's capacity to transmit => arbitrarily selected signals, which need not be "symbolic" (need not mean => or stand for anything). To use the term "information" in this connection => seems Pickwickian at least. "Real information"? Do you mean the => Carnap/Bar-Hillel program as taken up by Hintikka? Are you saying that => the latter has a useful representation of the meaning of texts? The Carnap and Bar-Hillel approach is based on the idea that the information conveyed by an assertion is that the actual world is a model of the sentence (or: "... is a member of the class of possible worlds in which sentence is true", or: "the present situation is a situation in which the sentence is true", or: ). This is certainly the most popular formal account of information. They, and Hintikka, count state descriptions to actually calculate the amount of information an assertion conveys, but that's just because Carnap (and, I suppose, the others) are interested in the logical notion of probability. If you start with a probability measure over structures (or possible worlds, or situations, or ... ) as given, you can be much more elegant--see, e.g., Scott and Krauss's paper on probabilities over L-omega1-omega-definable classes of structures. (It's in one of those late-60's North-Holland "Studies in Logic" volumes on inductive logic, maybe _Aspects of Inductive Logic_.) I don't recall what, if anything, you said about the application you have in mind, but, as the dynamic logic crowd discovered, L-omega1-omega is a natural language for talking about computation in general. => Bruce Nevin => bn@cch.bbn.com => -- rar ------------------------------ Date: 13 Jun 88 16:11:58 GMT From: ames!smu!ti-csl!mips.ti.com!fordyce@spam.istc.sri.com (David Fordyce) Subject: OBJECT-ORIENTED DATABASE WORKSHOP: OOPSLA '88 Article-I.D.: ti-csl.51420 OBJECT-ORIENTED DATABASE WORKSHOP To be held in conjunction with the OOPSLA '88 Conference on Object-Oriented Programming: Systems, Languages, and Applications 26 September 1988 San Diego, California, U.S.A. Object-oriented database systems combine the strengths of object-oriented programming languages and data models, and database systems. This one-day workshop will expand on the theme and scope of a similar OODB workshop held at OOPSLA '87. The 1988 Workshop will consist of the following four panels: Architectural issues: 8:30 AM - 10:00 AM Therice Anota (Graphael), Gordon Landis (Ontologic), Dan Fishman (HP), Patrick O'Brien (DEC), Jacob Stein (Servio Logic), David Wells (TI) Transaction management for cooperative work: 10:30 AM - 12:00 noon Bob Handsaker (Ontologic), Eliot Moss (Univ. of Massachusetts), Tore Risch (HP), Craig Schaffert (DEC), Jacob Stein (Servio Logic), David Wells (TI) Schema evolution and version management: 1:30 PM - 3:00 PM Gordon Landis (Ontologic), Mike Killian (DEC), Brom Mehbod (HP), Jacob Stein (Servio Logic), Craig Thompson (TI), Stan Zdonik (Brown University) Query processing: 3:30 PM - 5:00 PM David Beech (HP), Paul Gloess (Graphael), Bob Strong (Ontologic), Jacob Stein (Servio Logic), Craig Thompson (TI) Each panel member will present his position on the panel topic in 10 minutes. This will be followed by questions from the workshop participants and discussions. To encourage vigorous interactions and exchange of ideas between the participants, the workshop will be limited to 60 qualified participants. If you are interested in attending the workshop, please submit three copies of a single page abstract to the workshop chairman describing your work related to object-oriented database systems. The workshop participants will be selected based on the relevance and significance of their work described in the abstract. Abstracts should be submitted to the workshop chairman by 15 August 1988. Participants selected will be notified by 5 September 1988. Workshop Chairman: Dr. Satish M. Thatte Director, Information Technologies Laboratory Texas Instruments Incorporated P.O. Box 655474, M/S 238 Dallas, TX 75265 Phone: (214)-995-0340 Arpanet: Thatte@csc.ti.com CSNet: Thatte%ti-csl@relay.cs.net Regards, David ------------------------------ Date: 16 Jun 88 21:06:21 GMT From: nyser!cmx!skolem!kabowen@itsgw.rpi.edu (Ken Bowen) Subject: LP'88 Conference Announcement LP'88: 5th Conference on Logic Programming & 5th Symposium on Logic Programming August 15-19, 1988 University of Washington, Seattle, Washington Information and telephone(credit card) registration: Conference Registration, University of Washington: (206)-543-2310 ##TUTORIALS (All week): INTRODUCTION TO PROLOG (Mon, 8/15 -- 8:30-5:00) Christopher Mellish, University of Edinburgh An introduction to Prolog for engineers, programmers, and scien- tists with no background in the language. Tutorial Text: Pro- gramming in Prolog, 3rd ed. W. Clocksin & C. Mellish, Springer- Verlag. ABSTRACT INTERPRETATION (Mon 8/15 -- 1:30-5:00) Maurice Bruynooghe, Universiteit Leuven Directed at the advanced Prolog programmer, the tutorial will develop a general framework for extracting global properties of logic programs (e.g., mode & type inferencing, detecting deter- minism) via the use of abstract interpretation. The course will sketch: (1) A formal framework for abstract interpretation of logic programs which relies on familiar notions about the execu- tion of logic programs and uses only a small amount of mathemati- cal machinery concerning partial orders; (2) The process of developing an application within this framework; (3) High-level comments on the structure of a correctness proof of an applica- tion. IMPLEMENTATION OF PROLOG (Tues, 8/16 -- 8:30-12:00) D.H.D. War- ren, Manchester Univ. This tutorial presents the detailed design of the Prolog engine, now known as the WAM. The tutorial provides a detailed under- standing of the WAM and why WAM-based Prolog systems are effi- cient. It also gives insight into how to write efficient Prolog programs for WAM-based compilers. Attendees should know basic Prolog programming and it would help to have some familiarity with compiler technology. PARALLEL EXECUTION SCHEMES (Thurs, 8/18 -- 8:30-5:00) L. Kale', Univ. of Illinois This tutorial will describe the individual schemes for parallel execution of logic programs that have been proposed so far, and develop an understanding of their place in the spectrum along the dimensions of: degree of parallelism, overhead, targeted applica- tions, and type of multi-processor best suited for the scheme. The tutorial will be of interest to anyone planning to build a parallel logic programming system, as well as beginning research- ers in the area. A basic knowledge of logic programming will be presumed CONSTRAINT LOGIC PROGRAMMING (Tues, 8/16 -- 1:30-5:00) J-L. Lassez et al., IBM CLP offers a framework to reason with and about constraints in the context of Logic Programming. The fundamental principles of this paradigm are presented in order to illustrate the expressive power of constraints and to show how they naturally merge with a Logic Programming rule-based system. Next the design and imple- mentation of a CLP system is discussed, focusing on efficiency issues of constraint solving, followed by the descriptions of several applications. A basic knowledge of Prolog is presumed. CLP AND OPTIONS TRADING (Wed 8/17 -- 8:30-12:00) Catherine Lassez, IBM and Fumio Mizoguchi, Science Univ. of Tokyo This tutorial will explore the application of Constraint Logic Programming (CLP) to financial problems, in particular to options trading. The chosen examples will demonstrate the special strengths of combined symbolic and numeric constraint-oriented reasoning in a logic programming setting. Knowledge of CLP or attendance at the "Introduction to CLP" tutorial is essential for this course. LOGIC PROGRAMMING & LEGAL REASONING (Wed 8/17 -- 8:30-12:00) Robert Kowalski, Imperial College, and Marek Sergot, Imperial College The unique charateristics of legal reasoning are apparent not only in legal domains, but underlie administrative procedures and many data processing applications. The use of logic for analyz- ing legal reasoning has a long tradition. Computer implementa- tion of legal reasonoing involves representing and reasoning with legal language, the relationship between rules and regulations, and the policies they implement. The tutorial will examine the use of logic programming for analyzing such questions, for both real and hypothetical cases. PRACTICAL PROLOG FOR REAL PROGRAMMERS (Thurs, 8/18 -- 1:30-5:00) Richard O'Keefe, Quintus Computer Systems This tutorial assumes that you understand the elementary aspects of Prolog programming, such as recursion, pattern matching, par- tial data structures, and so on, and want to know how to use Pro- log to build practical programs. Topics covered will include "choice points and how to use the cut", "setofPhow it works and what it is good for", "efficient data structures", "mixed language programming", and "programming methodology". All topics will be illustrated by working code. LOGIC GRAMMARS FOR NL& COMPILING (Fri, 8/19 -- 8:30-12:00) Har- vey Abramson, Univ. of British Columbia This tutorial assumes a basic knowledge of Logic Programming techniques, but does not assume a detailed knowledge either of linguistics or of compilation techniques. The tutorial will show how logic programming naturally applies to both natural and for- mal grammars. Tutorial topics include: 1) Use of Metamorphosis Grammars and Definite Clause Grammars to produce derivation trees and semantic transforms; 2) Use of related grammar formalisms; 3) Compilation from natural language to logical form and from programming languages to machine code using Definite Clause Translation Grammars, a logical version of Attribute Grammars; 4) Top-down versus bottom-up parsing, chart-parsing, and the use of parallelism and concurrency. ##INVITED SPEAKERS: Layman E. Allen (U. Mich) Multiple Logical Interpretations of Legal Rules: Impediment or Boon forExpert Systems? William F. Bayse (FBI) Law Enforcement Applications of Logic Pro- gramming Alan Bundy (U. Edinburgh) A Broader Interpretation of Logic in Logic Programming Giorgio Levi (U. Pisa) Models, Unfolding Rules, and Fixpoint Se- mantics Carlo Zaniolo (MCC) Design & Implementation of a Logic-Based Language for Data Intensive Applications OVERALL SCHEDULE: Sunday (8/14): 3:30-5:30: Registration 5:30-- : Informal reception Monday (8/15): 9:00-9:30: Opening Session 9:30-10:30 Layman Allen 10:30-11:00 Break 11:00-12:30: Paper sessions: LP & FP #1; E & V #1; Imp #1 12:30-2:00: Lunch 2:00-3:30: Paper sessions: PrE #1; SemN#1; OR// #1 3:30-4:00: Break 4:00-5:30: Paper sessions: Ap #1; SemI #1; Imp #2 Tentative: Panel on Prolog Standards 5:30-- : Conference reception Tuesday (8/16): 8:30-10:00: Paper sessions: PrS; Cx + MT; //C #1 10:00-10:30: Break 10:30-12:00: Paper sessions: Obj + E & V #2; RP #1; //C #2 12:00-1:30: Lunch 1:30-3:30: Paper sessions: Meta; CN + GP #1; OR// #2 3:30-4:00: Break 4:00-5:00: Giorgio Levi 7:30-- : Demonstrations Wednesday (8/17): 8:30-10:00: Paper sessions: AbI # 1; &-OR// #1; GP #2 10:00-10:30: Break 10:30-12:00: Alan Bundy 12:00-1:30: Lunch 1:30-- : Free afternoon Thursday (8/18): 8:30-10:00: Paper sessions: LP&FP#2 + Db#1; RP#2+Types#1; //C # 3 10:00-10:30: Break 10:30-12:00: Paper sessions: Ap #2; Imp #3; SemN #2 12:00-1:00: Lunch 1:00-2:30: Paper sessions: Db #2; SemI#2+Time; Types #2 2:30-3:30: Paper sessions: UC; PrE #2; &-// 3:30-4:00: Break 4:00-5:00: William Bayse 5:30-- : Conference Dinner--Speaker: J. Alan Robinson Friday (8/19): 8:30-10:00: Paper sessions: Ap #3; AbI #2; &-OR// #2 10:00-10:30: Break 10:30-11:30: Carlo Zanielo 11:30-12:00: Panel/Closing session ##CONTRIBUTED PAPERS: %%APPLICATIONS & PROGRAMMING METHODOLOGY * (Ap) Applications P.G. Bosco, C. Cecchi and C. Moiso, Exploiting the Full Power of Logic Plus Functional Programming (#1) Tony Kusalik and C. McCrosky, Improving First-Class Array Expres- sions Using Prolog (#1) Toramatsu Shintani, A Fast Prolog-based Inference Engine KORE/IE (#1) M. Dincbas, H. Simonis and P. van Hentenryck, Solving a Cutting- Stock Problem in Constraint Logic Programming (#2) Catherine Lassez and Tien Huynh, A CLP(R) Option Analysis Sys- tem(#2) Peter B. Reintjes, A VLSI Design Environment in PROLOG (#2) T.W.G. Docker, SAME - A Structured Analysis Tool and its Imple- mentation in Prolog (#3) Kevin Steer, Testing Data Flow Diagrams with PARLOG (#3) *(CN) Constructive negation David Chan, Constructive Negation Based on the Completed Database Adrian Walker, Norman Foo, Andrew Taylor and Anand Rao, Deduced Relevant Types and Constructive Negation (Db) Databases Raghu Ramakrishnan, Magic Templates: A Spellbinding Approach to Logic Programming (#1) P. Franchi-Zannettacci and I. Attali, Unification-free Execution of TYPOL Programs by Semantic Attributes Evaluation (#2) D.B. Kemp and R.W. Topor, Completeness of a Top-Down Query Evaluation Procedure for Stratified Databases (#2) Hirohisa Seki and Hidenori Itoh, An Evaluation Method of Strati- fied Programs under the Extended Closed World Assumption (#2) *(GP) Grammar & Parsing R. Trehan and P.F. Wilk, A Parallel Chart Parser for the Commit- ted Choice Non-Deterministic (CCND) Logic Languages (#1) Harvey Abramson, Metarules and an Approach to Conjunction in De- finite Clause Translation Grammars: Some Aspects of... (#2) Veronica Dahl, Representing Linguistic Knowledge through Logic Programming (#2) Lynette Hirschman, William C. Hopkins and Robert Smith, OR- Parallel Speed-up in Natural Language Processing: A Case Study (#2) *(LP&FP) Logic & Functional programming Jean H. Gallier and Tomas Isakowitz, Rewriting in Order-sorted Equational Logic (#1) Claude Kirchner, Order-Sorted Equational Unification (#1) Joseph L. Zachary, A Pragmatic Approach to Equational Logic Pro- gramming (#1) Staffan Bonnier and Jan Maluszynski, Towards a Clean Amalgamation of Logic Programs with External Procedures (#2) Steffen Holldobler, From Paramodulation to Narrowing (#2) *(Meta) Meta-programming A. Bruffaerts and E. Henin, Proof Trees for Negation as Failure or Yet Another Prolog Meta-Interpreter Patrizia Coscia, Paola Franceschi, Giorgio Levi et. al., Meta- Level Definition and Compilation of Inference Engines in the Ep- silon Logic Programming Environment C.S. Kwok and M.J. Sergot, Implicit Definition of Logic Programs Arun Lakhotia and Leon Sterling,Composing Prolog Meta- Interpreters *(Obj) Objects Weidong Chen and D.S. Warren, Objects as Intensions John S. Conery, Logical Objects * (PrE) Programming environments Miguel Calejo and Luis Moniz Pereira, A Framework for Prolog De- bugging (#1) Dave Plummer, Coda: An Extended Debugger for PROLOG (#1) Ehud Shapiro and Yossi Lichtenstein, Abstract Algorithmic Debug- ging (#1) Mike Brayshaw and Marc Eisenstadt, Adding Data and Procedure Abstraction to the Transparent Prolog Machine (TPM) (#2) Michael Gorlick and Carl Kesselman, Gauge: A Workbench for the Performance Analysis of Logic Programs (#2) *(PrS) Problem-solving & novel techniques Jonas Barklund, Nils Hagner and Malik Wafin, Condition Graphs Philippe Codognet, Christian Codognet and Gilberto File, Yet Another Intelligent Backtracking Method Sei-ichi Kondoh and Takashi Chikayama, Macro Processing in Prolog *(Time) Temporal reasoning Kave Eshghi, Abductive Planning with Event Calculus *(Ty) Types Paul Voda, Types of Trilogy (#1) M.H. van Emden, Conditional Answers for Polymorphic Type Infer- ence (#2) Uday S. Reddy, Theories of Polymorphism for Predicate Logic Pro- grams (#2) Jiyang Xu and David S. Warren, A Type Inference System for Prolog (#2) *(UC) Unification & constraints D. Scott Parker and R.R. Muntz, A Theory of Directed Logic Pro- grams and Streams Graeme S. Port, A Simple Approach to finding the Minimal Subsets of Equations Needed to Derive a Given Equation by Unification %%THEORY & PROGRAM ANALYSIS *(AbI) Abstract interp. & data dependency Maurice Bruynooghe and Gerda Jenssens, An Instance of Abstract Interpretation Intergrating Type and Mode Inferencing, Part1: the abstract domain (#1) Manuel Hermenegildo, Richard Warren & Saumya Debray, On the Prac- ticality of Global Flow Analysis of Logic Programs (#1) Annika Waern, An Implementation Technique for the Abstract In- terpretation of Prolog (#1) Saumya Debray, Static Analysis of Parallel Logic Programs (#2) Kim Marriott and Herald Sondergaard, Bottom-up Abstract Imterpre- tation of Logic Programs (#2) Will Winsborough and Annika Waern, Transparent And-Parallelism in the Presence of Shared Free Variables (#2) *(Cx) Complexity K.R. Apt and Howard A. Blair, Arithmetic Classification of Per- fect Models of Stratified Programs Stephane Kaplan, Algorithmic Complexity of Logic Programs *(E&V) Extensions and variations of LP Donald Loveland and Bruce T. Smith, A Simple Near-Horn Prolog In- terpreter (#1) Dale Miller and Gopalan Nadathur, An Overview of l-PROLOG (#1) Jack Minker, Jorge Lobo and Arcot Rajasekar, Weak Completion Theory for Non-Horn Programs (#1) Bharat Jayaraman and Anil Nair, Subset-logic Programming: Appli- cation and Implementation (#2) *(RP) Reasoning about programs Charles Elkan and David McAllester, Automated Inductive Reasoning about Logic Programs (#1) Laurent Fribourg, Equivalence-Preserving Transformations of In- ductive Properties of Prolog Programs (#1) K. Marriott, L. Naish and J.L. Lassez, Most Specific Logic Pro- grams (#1) H. Fujita, A. Okumura and K. Furukawa, Partial Evaluation of GHC Programs Based on UR-set with Constraint Solving (#2) John Hannan and Dale Miller, Uses of Higher-Order Unification for Implementing Program Transformers (#2) *(SemI) Semantic issues Aida Batarekh and V.S. Subrahmanian, Semantical Equivalences of (non-Classical) Logic Programs (#1) Kenneth Kunen, Some Remarks on the Completed Database (#1) Maurizio Martelli, M. Falaschi, G. Levi and C. Palamidessi, A New Declarative Semantics for Logic Languages (#1) D. Pedreschi and P. Mancarella, An Algebra of Logic Programs (#2) Stan Raatz and Jean H. Gallier, A Relational Semantics for Logic Programming (#2) V. S. Subrahmanian, Intuitive Semantics for Quantitative Rule Sets (#2) * (SemN) Semantics of negation Melvin Fitting and Miriam Ben-Jacob, Stratified and Three-valued Logic Programming Semantics (#1) Vladimir Lifschitz and Michael Gelfond, The Stable Model Seman- tics for Logic Programming (#1) Teodor Przymusinski, Semantics of Logic Programs and Non- monotonic Reasoning (#1) Yves Moinard, Pointwise Circumscription is Equivalent to Predi- cate Completion (sometimes) (#2) Halina Przymusinska and Teodor Przymusinski, Weakly Perfect Model Semantics for Logic Programs (#2) * (MT) Miscellaneous Theory M.A. Nait Abdallah, Heuristic Logic and the Process of Discovery ##IMPLEMENTATION & PARALLELISM * (&//) AND-parallelism V. Kumar and Y-J Lin, AND-parallel Execution of Logic Programs on a Shared Memory Multoprocessor: A Summary of Results Kotagiri Ramamohanarao and Zoltan Somogyi, A Stream AND-Parallel Execution Algorithm with Backtracking *(& - OR //) AND-OR parallelism P. Biswas, Su and Yun, A Scalable Abstract Machine Model to Sup- port Limited OR (LOR)/Restricted-AND Parallelism (RAP) in Logic Programs (#1) K.W. Ng and H.F. Leung, The Competition Model for Parallel Execu- tion of Logic Programs (#1) Prabhakaran Raman and Eugene W. Stark, Fully Distributed, AND-OR Parallel Execution of Logic Programs (#1) P. Biswas and Tseng, A Data-Driven Parallel Execution Model for Logic Programs (#2) Jacques Chassin de Kergommeaux and Philippe Robert, An Abstract Machine to Implement Efficiently OR-AND Parallel Prolog (#2) L.V. Kale, B. Ramkumar and W.W. Shu, A Memory Organization In- dependent Binding Environment for AND and OR Parallel Execution of Logic Programs (#2) * (Imp) Implementation Hamid Bacha, MetaProlog Design and Implementation (#1) Gerda Janssens, Bart Demoen & Andre Marien, Register Allocation for WAM, Based upon an Adaptable Unification Order (#1) Jonathan Mills and Kevin Buettner, Assertive Demons (#1) D.A. Chu and F.G. McCabe, SWIFT - a New Symbolic Processor (#2) Subash Shankar, A Hierarchical Associative Memory Architecture for Logic Programming Unification (#2) Charles Stormon, Mark Brule and John Oldfield et. al., An Architecture Based in Content-Addressable Memory for the Rapid Execution of Prolog (#2) David Hemmendinger, A Compiler and Semantic Analyzer Based on Categorial Grammars (#3) Feliks Kluzniak, Compile Time Garbage Collection for Proportional Prolog (#3) K. Kurosawa, S. Yamaguchi, S. Abe and T. Bandoh, Instruction Architecture for High Performance Integrated Prolog Processor IPP (#3) *(Or//) OR-parallelism and parallel Prolog Khayri Ali, OR-Parallel Execution of Prolog on BC-Machine (#1) Lee Naish, Parallelizing NU-Prolog (#1) Ross Overbeek, Mats Carlsson and Ken Danhof, Practical Issues Re- lating to the Internal Database Predicates in an OR-Parallel Pro- log: .... (#1) Hiyan Alshawi and D.B. Moran, The Delphi Model and some Prelim- inary Experiments (#2) Ewing Lusk, Ralph Butler, Terry Disz and Robert Olsen et. al., Scheduling OR-Parallelism: an Argonne Perspective (#2) *(// C) Concurrent sys: GHC, Parlog, CP etc. Atsuhiro Goto, Y. Kimura, T. Nakagawa and T. Chikayama, Lazy Reference Counting - An Incremental Garbage Collection Method for Parallel Inference Machines (#1) Hamish Taylor, Localising the GHC Suspension Test (#1) Handong Wu, An Extended Dataflow Model of FGHC (#1) Leon Alkalaj and Ehud Shapiro, An Architectural Model for a Flat Concurrent Prolog Processor (#2) V.J. Saraswat, A Somewhat Logical Formulation of CLP Synchronisa- tion Primitives (#2) S. Klinger and E. Shapiro, A Decision Tree Compilation Algorithm for Flat Concurrent Prolog (#3) Martin Nilsson and Hidehiko Tanaka, A Flat GHC Implementation for Supercomputers (#3) Sven-Olof Nystrom, Control Structures for Guarded Horn Clauses (#3) **Conference Registration Information: **CONFERENCE REGISTRATION FEES: Advance (until 1 July): ALP/IEEE member: Regular: $240 Student: $75 Non-member: Regular: $320 Student: $95 Late (after 1 July): ALP/IEEE member: Regular: $340 Student: $105 Non-member: Regular: $455 Student: $135 **TUTORIAL REGISTRATION FEES: Advance (until 1 July): Full Day Tutorials: ALP/IEEE member: Regular: $300 Student: $180 Non-member: $400 Half Day Tutorials: ALP/IEEE member: Regular: $150 Student: $90 Non-member: $200 Late (after 1 July): ALP/IEEE member: Regular: $360 Student: $215 Non-member: $480 Half Day Tutorials: ALP/IEEE member: Regular: $180 Student: $140 Non-member: $240 ------------------------------ End of AIList Digest ********************