Subject: From: AIList Moderator Kenneth Laws <AIList-REQUEST@SRI-AI>

From: [email-masked]
Date: Mon, 24 Jun 85 21:59:18 edt

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Subject: AIList Digest   V3 #82
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AIList Digest            Monday, 24 Jun 1985       Volume 3 : Issue 82

Today's Topics:
  Queries - VAX Lisp & PC Lisps & McDonnell Douglas NL Breakthrough,
  Games - Optimal Scrabble,
  Automata - Predation/Cooperation,
  Psychology - Common Sense,
  Analogy - Bibliography,
  Seminar - Evaluating Expert Forecasts (NASA)

----------------------------------------------------------------------

Date: Mon, 24 Jun 85 07:38:35 EDT
From: cugini@NBS-VMS
Subject: VAX Lisp

Just looking for a little consumer information here - does anyone have
any experience with Digital's VAX LISP ?  DEC advertises it as a
full-fledged implementation of CommonLisp.  Any remarks on price,
performance, quality, etc are appreciated.

John Cugini <Cugini@NBS-VMS>
Institute for Computer Sciences and Technology
National Bureau of Standards
Bldg 225 Room A-265
Gaithersburg, MD 20899
phone: (301) 921-2431

------------------------------

Date: Sun 23 Jun 85 15:09:12-EDT
From: Jonathan Delatizky <[email-masked]>
Subject: PC Lisps

           [Forwarded from the MIT bboard by SASW@MIT-MC.]

Can some of you out there who have used Lisp implementations on IBM PC
type machines give me some recommendations as to the best PC Lisp? I
plan to run it on a PC/XT and a PC/AT if possible. Also, any expert systems
shells that run on the same machines, real or toy-like.

...jon

------------------------------

Date: 22 Jun 1985 13:20-EST
From: George Cross <[email-masked]>
Subject: McDonnell Douglas NL Breakthrough

         The following is the text of a full page color ad on page 49
         in the June 24, 1985 New Yorker.  It has also been run in the
         Wall Street Journal.  Does anyone know what the breakthrough
         is?  This was mentioned on the ailist some time ago but I
         didn't notice a response.  There is a photo of a hand holding
         the chin of smiling boy.

BREAKTHROUGH: A COMPUTER THAT UNDERSTANDS YOU LIKE YOUR MOTHER

Having to learn letter-perfect software languages can be frustrating to the
average person trying to tap the power of a computer.

But practical thinkers at our McDonnell Douglas Computer Systems Company
have created the first computer that accepts you as you are - human.

They emulated the two halves of the brain with two-level software: One level
with a dictionary of facts and a second level to interpret them.  The
resulting Natural Language processor understands everyday conversational
English.  So it knows what you mean, no matter how you express yourself.  It
also learns your idiosyncrasies, forgives your errors, and tells you how to
find out what you're looking for.

Now, virtually anyone who can read and write can use a computer.

We're creating breakthroughs not only in Artificial Intelligence but also in
health care, space manufacturing and aircraft.

We're McDonnell Douglas.

How can I learn more?
Write
        P.O. Box 19501
        Irvine, CA 92713

------------------------------

Date: 22 Jun 1985 13:07-EDT
From: [email-masked]
Subject: Optimal Scrabble

Anyone interested in computer Scrabble should be aware that Guy
Jacobson and Andrew Appel (some of the people that did Rog-o-matic)
have written a program which in some sense solves the problem.  Using a
clever data structure, their program makes plays in a few seconds and
always makes the best possible play.  Their dictionary is the official
Scrabble dictionary.  The program is not completely optimal because it
doesn't take into account how the placement of its words near things
like triple word scores may help the other player, but in all other
senses it always makes the best play.  I suppose some simple strategic
techniques could be added using a penalty function, but as the program
almost always wins anyway, this hasn't been done.  It regularly gets
bingos (all seven letters used), makes clever plays that create three
or more words, and so on.  The version they have now runs on Vax/Unix.
There was some work to port it to the (Fat) Macintosh but that is not
finished, mainly for lack of interest.

Jon

------------------------------

Date: Fri, 21 Jun 85 17:17:58 EDT
From: [email-masked]
Subject: Predation/Cooperation (AIL v3 #78)

Re: enquiry of sdmartin@bbng about learning cooperation in predation:
For an extensive investigation of a minimal-domain model (prisoner's
dilemma),see _The Evolution of Co-operation_ (NY: Basic Books, 1984;
LC 83-45255, ISBN 0-465-02122-0) by Robert Axelrod (of the U of Mich).
 He is in the Institute of Public Policy Studies, but one of his more
interesting methods was the use of the genetic algorithms of John
Holland (also of the U of Mich) to breed automata to have improved
strategies for playing Prisoner's dilemma.  A one-sentence summary of
his results is that cooperation can displace non-cooperation if
individuals remember each other's behavior and have a high enough
probability of meeting again. An intermediate-length summary can be
found in Science _211_ (27 Mar 81) 1390-1396.

------------------------------

Date: Fri 21 Jun 85 19:23:03-PDT
From: Calton Pu <[email-masked]>
Subject: definition of common sense

I had a discussion with a friend on this exact topic just a few weeks
ago.  My conclusions can be phrased as an elaboration of V. Pratt's
two criteria.

   1.   common knowledge basis (all facts depended on must be
        common knowledge)

I think the (abstract) common knowledge basis can be more concretely
described as "cultural background".  Your Formico's Pizza example
shows clearly that anybody not familiar with San Francisco will not
have the "common sense" to go there.  The term "cultural background"
admits many levels of interpretation (national, provincial, etc.)
so most of REALLY COMMON knowledge basis will be encompassed.

   2.   low computational complexity (easy to check the conclusion).

I think the key here is not the checking (NP), but the finding (P) of
the solution.  So here I differ from Vaughan, in that I believe common
sense is something "obvious" to a lot of people, by their own
reasoning power.

There are two factors involved: the first is the amount of reasoning
power; the second is the amount of deductive processing involved.  On
the first factor, unfortunately usual words to describe people with
the adequate reasoning power such as "sensible", "reasonable", and
"objective" have also the connotation of being "emotionless".  Let's
leave out the emotional aspects and use the term "reasonable" to
include everybody who is able to apply elementary logic to normal
situations.  On the second factor, typical words to picture easy
deductive efforts are "obvious", "clear", and "evident".

So my definition of common sense is: that which is obvious to a
reasonable person with an adequate cultural background.

I should point out that the three parameters of common sense, cultural
background, reasoning power, and deductive effort, vary from place to
place and from person to person.  If we agreed more on each other's
common sense, it might be easier to negotiate peace.

------------------------------

Date: Monday, 24 Jun 85 01:38:08 EDT
From: shrager (jeff shrager) @ cmu-psy-a
Subject: Analogy Bibliography

[Someone asked for an analogy bibliography a while back.  This was compiled
about two years (maybe more) ago so it's partial and somewhat out of date,
but might serve as a starter for people interested in the topic.  I've added
a couple of thing just now in looking it over.  The focus is primarily
psychological, but readers will recognize some of the principle AI work as
well. I've got annotations for quite a few of these, but the remarks are
quite long and detailed so I won't burden AIList with them. -- Jeff]

                                    ANALOGY
                           (A partial bibliography)

                           Compiled by Jeff Shrager
                                CMU Psychology
                                 24 June 1985

               (Send recommendations to Shrager@CMU-PSY-A.)

Bobrow,  D.  G.  &  Winograd,  T.    (1977).  An  Overview  of KRL: A Knowledge
     Representation Language.  Cognitive Science, 1, 3-46.

Bott, R.A.  A study of complex learning: Theories and Methodologies.  Univ.  of
     Calif.  at  San  Diego, Center for Human Information Processing report No.
     7901.

Brown, D.  (1977). Use of Analogy to Acheive New Experience.  Technical  Report
     403, MIT AI Laboratory.

Burstein,  M.  H.    (June,  1983). Concept Formation by Incremental Analogical
     Reasoning  and  Debugging.    Proceedings  of  the  International  Machine
     Learning Workshop.  pp. 19-25.

Carbonell,  J.  G.  (August, 1981). A computational model of analogical problem
     solving.  Proceedings of the Seventh  International  Joint  Conference  on
     Artificial Intelligence, Vancouver.  pp. 147-152.

Carbonell,  J.G.    (1983).  Learning  by Analogy: Formulating and Generalizing
     Plans from Past Experience.    In  Michalski,  R.S.,  Carbonell,  J.G.,  &
     Mitchell,  T.M.  (Ed.),  Machine  Learning,  an  Aritificial  Intelligence
     Approach  Palo Alto:  Tioga Press.

Carnap, R.  (1963).  Variety,  analogy  and  periodicity  in  inductive  logic.
     Philosophy of Science, 30, 222-227.

Darden,   L.     (June,  1983).  Reasoning  by  Analogy  in  Scientific  Theory
     Construction.  Proceedings of the International Machine Learning Workshop.
     pp. 32-40.

de Kleer, J. & Brown, J.S.  Foundations of Envisioning.  Xerox PARC report.

Douglas,  S. A., & Moran, T. P.  (August, 1983). Learning operator semantics by
     analogy.    Proceedings  of  the   National   Conference   on   Artificial
     Intelligence.

Douglas,  S.  A.,  &  Moran,  T.  P.    (December, 1983b). Learning text editor
     semantics by analogy.  Proceedings of  the  Second  Annual  Conference  on
     Computer Human Interaction.  pp. 207-211.

Dunker, K.  (1945). On Problem Solving.  Psychological Monographs, 58, .

Evans,  T.  G.    (1968).  A  program  for the solution of a class of geometric
     analogy intelligence test  questions.    In  Minsky,  M.  (Ed.),  Semantic
     Information Processing  Cambridge, Mass.:  MIT Press.  pp. 271-253.

Gentner,  D.    (July,  1980).  The  Structure of Analogical Models in Science.
     Report 4451, Bolt Beraneck and Newman.

Gentner, D.  (1981).  Generative Analogies as Mental Models.    Proceedings  of
     the  3rd  National Cognitive Science Conference.  pp. 97-100.  Proceedings
     of the 3rd annual conference.

Gentner, D.  (1982). Are Scientific Analogies Metaphors?  In D. S. Miall (Ed.),
     Metaphor:  Problems and Perspectives  New York:  Harvester Press Ltd.  pp.
     106-132.

Gentner, D., & Gentner, D. R.  (1983). Flowing Waters or Teeming Crowds: Mental
     Models  of  Electricity.    In  Gentner, D. & Stevens, A. L. (Ed.), Mental
     Models  Hillsdale, NJ:  Lawrence Earlbaum Associates.  pp. 99-129.

Gick, M. L. & Holyoak, K. J.  (1980).  Analogic  Problem  Solving.    Cognitive
     Psychology, 12, 306-355.

Gick,  M.  L. & Holyoak, K. J.  (1983). Schema Induction and Analogic Transfer.
     Cognitive Psychology, 15, 1-38.

Halasz, F. & Moran, T. P.  (1982).  Analogy Considered Harmful.  Proceedings of
     the Conference on Human Factors in Computer Systems, New York.

Hesse,  Mary.    (1955).    Science  and  the  Human  Imagination.    New York:
     Philisophical Library.

Hesse, Mary.  (1974).  The Structure of Scientific Inference.   Berkeley: Univ.
     of Calif. Press.

Kling,  R.  E.    (1971).  A  Paradigm  for  Reasoning  by Analogy.  Artificial
     Intelligence, 2, 147-178.

Lenat, D.B. & Greiner, R.D.  (1980).  RLL: A representation language  language.
     Proc. of the first annual meeting.  Stanford.

McDermott,  J.    (December,  1978).  ANA:  An  assimilating  and accomodatiing
     production  system.    Technical  Report  CMU-CS-78-156,   Carnegie-Mellon
     University.

McDermott,  J.    (1979).   Learning to use analogies.  Sixth Internation Joint
     Conference on Artificial Intelligence.

Medin, D. L. and Schaffer, M. M.   (1978).  Context  Theory  of  Classification
     Learning.  Psychological Review, 85(3), 207-238.

Minsky,  M.   (1975). A Framework for Representing Knowledge.  In Winston, P.H.
     (Ed.), The Psychology of Computer Vision  New York:  McGraw Hill.

Minsky, M.  (July, 1982). Learning Meaning.  Technical Report, .    Unpublished
     MIT AI Lab techinical report.

Moore, J. & Newell, A.  (1974). How can MERLIN Understand?  In L.W.Gregg (Ed.),
     Knowledge and Cognition  Potomic, Md.:  Erlbaum Associates.

Ortony, A.  (1979). Beyond Literal Similarity.  Psych Review, 86(3), 161-179.

Pirolli, P. & Anderson, J.R. (1985) The role of Learning from Examples in the
     Acquisition of Recursive Programming Skills.  Canadian Journal of
     Psychology. Vol. 39, no. 4; pgs. 240-272.

Polya, G.  (1945).  How to solve it.   Princton, N.J.: Princeton U. Press.

Quine, W. V. O.  (1960).  Word and Object.   Cambridge: MIT Press.

Reed, S. K., Ernst, G. W., & Banerji, R.    (1974).  The  Role  of  Analogy  in
     Transfer  Between  Similar  Problem  States.    Cognitive  Psychology,  6,
     436-450.

Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D.  M.,  &  Boynes-Braem,  P.
     (1976). Basic Objects on Natural Kinds.  Cog Psych, 8, 382-439.

Ross,  B.   (1982). Remindings and Their Effects in Learning a Cognitive Skill.
     PhD thesis, Stanford.

Rumelhart,  D.E.,  &  Norman,  D.A.      (?DATE?).   Accretion,   tuning,   and
     restructuring:  Three  modes  of  learning.    In R.Klatsky and J.W.Cotton
     (Eds.),  Semantic  Factors  in  Cognition    Hillsdale,  N.J.:     Erlbaum
     Associates.

Rumerlhart,  D.E. & Norman, D.A.  (1981). Analogical Processes in Learning.  In
     J.R. Anderson (Ed.), Cognitive Skills and Their  Acquisition    Hillsdale,
     N.J.:  Lawrence Earlbaum Associates.  pp. 335-360.

Schustack, M., & Anderson, J. R.  (1979). Effects of analogy to prior knowledge
     on memory for new information.  Journal  of  Verbal  Learning  and  Verbal
     Behavior, 18, 565-583.

Sembugamoorthy,  V.    (August,  1981). Analogy-based acquisition of utterances
     relating to temporal aspects.  Proceedings of the 7th International  Joint
     Conference on Artificial Intelligence.  pp. 106-108.

Shrager,  J.  &  Klahr,  D.    (December,  1983).  A  Model  of Learning in the
     Instructionless Environment.   Proceedings  of  the  Conference  on  Human
     Factors in Computing Systems.  pp. 226-229.

Shrager,  J.  &  Klahr, D.  Instructionless Learning: Hypothesis Generation and
     Experimental Performance.  In preparation.

Sternberg, R.  (1977).  Intelligence, information  processing,  and  analogical
     reasoning:  The  componential  analysis  of  human abilities.   Hillsdale,
     N.J.: Lawrence Erlbaum Associates.

VanLehn, K., & Brown, J. S.    (1978).  Planning  nets:  A  representation  for
     formalizing  analogies and semantic models of procedural skills.  In Snow,
     R. E., Frederico, P. A. and Montague, W. E. (Ed.), Aptitude  Learning  and
     Instruction:  Cognitive Process Analyses  Hillsdale, NJ:  Lawrence Erlbaum
     Associates.

Weiner, E. J.  A Computational Approach to Metaphore  Comprehension.    In  the
     Penn Review of Linguistics.

Winston,   P.  H.    (December,  1980).  Learning  and  Reasoning  by  Analogy.
     Communications of the ACM, 23(12), 689-703.

Winston, P. H.  Learning and Reasoning by Analogy: The details.   MIT  AI  Memo
     number 520.

------------------------------

Date: Fri, 21 Jun 85 11:42:26 pdt
From: [email-masked] (Amy Jo Bilson)
Subject: Seminar - Evaluating Expert Forecasts (NASA)

                           NASA

            PERCEPTION AND COGNITION SEMINARS

    Who:        Keith Levi
    From:       University of Michigan
    When:       10 am, Tuesday, June 25, 1985
    Where:      Room 177, Building 239, NASA Ames Research Center
    What:       Evaluating Expert Forecasts

    Abstract:   Probabilistic forecasts, often generated by an expert,
                are critical to many decision aids and expert systems.
                The quality of such inputs has usually been evaluated in
                terms of logical consistency.  However, in terms of
                real-world implications, the external correspondence of
                probabilistic forecasts is usually much more important
                than internal consistency.  I will discuss recently
                developed procedures for evaluating external correspondence
                and present research on the topic.

    Non-citizens (except permanent residents) must have prior approval from
    the Directors Office one week in advance. Permanent residents must show
    Alien Registration Card at the time of registration.

    To request approval or obtain further information, call 415-694-6584.

------------------------------

End of AIList Digest
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