Subject: From: AIList Moderator Kenneth Laws <AIList-REQUEST%sri-ai.arpa@CSNET-RELAY>

From: [email-masked]
Date: Mon, 24 Feb 86 04:05:54 est

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Subject: AIList Digest   V4 #35
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AIList Digest            Sunday, 23 Feb 1986       Volume 4 : Issue 35

Today's Topics:
  Games - Computer Othello & Computer Chess,
  Automata - Self-Replication and Artificial Life,
  Methodology - A Thought on Thinking,
  Humor - AI Koans & The Naive Dog Physics Manifesto

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

Date: 16 Feb 86 21:34:13 EST
From: [email-masked]
Subject: Computer Othello (Bill)

           [Forwarded from the CMU bboard by Laws@SRI-AI.]

In the recent North American Computer Othello Championship Tournament,
our department's entry, BILL, placed second in a field of 11.  The
final standings were:
        1.  Aldaron (C. Heath)          7.5 - 0.5
        2.  Bill (K. Lee & S. Mahajan)  7   - 1
        3.  Brand (A. Kierulf)          5   - 3
        3.  Fort Now (?)                5   - 3

Bill's only loss was to Aldaron, the defending champion, as well as
the program that should have beaten Iago in 1981.  However, Bill's
loss was due to the choice of color in the game with Aldaron.  In an
unofficial rematch with Aldaron, Bill won with the colors reversed.
Furthermore, Bill soundly defeated the program that tied Aldaron.

With the many improvements that we have in mind and the enthusiastic
participation this year, we expect an exciting championship next year.
If anyone is interested in more information about Bill, this tournament,
or the game transcripts, please send mail to kfl@speech2 or mahajan@h.

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

Date: 17 February 1986 1954-EST
From: Hans [email-masked]
Subject: computer chess (final)

           [Forwarded from the CMU bboard by Laws@SRI-AI.]

The Eastern  Team championship is  essentially over.  Hitech  won and
drew  today,  producing  a final  score  of  5.5  -  .5.   It  played
remarkably well.   Outside of falling  into an opening trap  due to a
deficiency in its book, and being outplayed a little in game four but
recovering  when  the opponent  made  an  error,  its play  is  above
criticism.  It played  mainly against expert  level players,  a class
that is almost extinct in Pittsburgh, and beat every one of them.  It
drew its final game with a strong master rated nearly equal (2291) to
Hitech.   It had  black in  4 games,  and white  in two;  a noticable
disadvantage.  Mike  Valvo who directs the ACM  tournaments played on
board one  for the  team and  finished with  a score  of 4.5  to 1.5.
Hitech played on  board two, and Belle played on  board three.  Belle
apparently has had a hardware  overhaul, and played  much better than
it had recently.  However, on  a comparison basis, Belle scored 5- 1,
losing in  the last  round, and it  had 4 whites  and two  blacks and
played against  slightly weaker  opponents than  Hitech.   The fourth
board human  on the team  was a  catastrophe, scoring less  than 50%.
The crucial  match was in  the fifth round and  ended in a  draw with
both computers winning and both  humans losing, thus making the match
a draw and ruining our chances of winning the title (the team had won
all its previous matches).  In  the final round, there are still some
unfinished games, but the team should do no worse than draw, giving a
team record of 5 -1 (two drawn  matches).  Overall, it is safe to say
that on our  team the species @u[robot sapiens]  far outperformed the
species @u[homo sapiens].

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

Date: Wed, 19 Feb 86 10:20:20 EST
From: [email-masked]
Subject: Artificial Life

    I read in the ailist a series of comments on the size of self-reproducing
systems (ARPA.AIList, Volume 3, Issue 71, 06/01/85 - starting with the
message from zim@mitre of 05/24/85 )
I have published an article wherein I exhibit a self-reproducing configuration
embedded in a cellular automaton which occupies a mere 10x15 cell rectangle.
The construction is based on a modification of one of Codd's components
(see Codd: Cellular Automata) in his simplification of von Neumann's
self-reproducing machine. My article is published in: Physica 10D (1984)
North Holland, pp 135-144, entitled 'self-reproduction in cellular automata'.

Basicly, this configuration consists of a looped pathway with a construction
arm extending out from one corner. Signals cycling around the loop cause
the construction arm to be extended by a certain amount and then cause a
90 degree corner to be built. as this sequence is executed 4 times (due
to the same signal sequence cycling around the loop 4 times), the four
sides of an offspring loop are built. When the extended construction arm
runs into itself, the resulting collision causes the two loops to detach
from each other and also triggers the construction of a new construction
arm on each loop. The new arm on the parent loop is located at the
next corner 'downstream' (in the sense of signal flow) from the original
site. Thus, the parent loop will go on to build another loop in a new
direction. Meanwhile, when the offspring was formed, a copy of the signal
sequence that serves as the description was trapped inside it when the
two detached from one another, thus it, too, goes on to build offspring.
The result is a growing colony of loops that expands out into the array,
consisting of a reproductive outer fringe surrounding a growing 'dead'
core, in the manner of a coral reef or the cross section of a tree.
Details are to be found in the article. Although this construction is
not capable of universal construction or computation, it clearly
reproduces itself in a non-trivial manner, unlike the reproduction under
modulo addition rules, of which Fredkin's reproducing system is an example.

I am also working on cellular automaton simulations of insect colonies and
artificial biochemistries. I have another article coming out in the proceedings
of the conference on 'Evolution, Games, & Learning' held at the Los Alamos
National Labs last May. It is entitled 'studying artificial life with cellular
automata'. There will be a video tape available soon from Aerial Press in
Santa Cruz which illustrates the self-reproducing loops as well as the
artificial insect colony simulations and other examples of `artificial life'.

I would be very interested in hearing from anybody who is working on anything
which might fall under the general heading 'artificial life'. I would also
like to try to get together a workshop, with computer support, where people
who have been working in this area could get together and have a 'jam session'
of sorts, and see each other's stuff. Any proceedings from such a workshop
would benefit greatly from having a video published along with it. If anybody
is interested in helping to organize such a workshop, send me a message. I
can be reached at: [email-masked]

       USPS:  Christopher G. Langton / EECS Dept. / University of Michigan /
              Ann Arbor MI 48109

       MA-BELL (now divorced from PA-ATT) 313-763-6491

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

Date: Sat, 15 Feb 86 15:09:11 GMT
From: [email-masked]
Subject: A Thought

                                From Vol 4 # 26:-  "The idea is
to get kids to be more thoughtful about thinking by getting them to
try to think about how animals think, and by taking the results of
these comtemplations and actually building animal-like creatures that
work." Alan Kay.
                                From Vol 3 # ??:- Date: Tue, 12 Mar 85
"Just as man had to study birds, and was able to derive the underlying
mechanism of flight, and then adapt it to the tools and materials
at hand, man must currently study the only animal that thinks
in order to derive the underlying principles there also." Frank Ritter

I am struck by two (or more?) very different uses of the word "think"!

Gordon Joly
ARPA: [email-masked]
UUCP: ...mcvax!ukc!kcl-cs!qmc-ori!gcj

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

Date: Thu, 13 Feb 86 00:08:02 PST
From: "Douglas J. Trainor" <[email-masked]>
Subject: a cuppla ai koans

from <[email-masked]>

One day SIMON was going to the cafeteria when he met WEIZENBAUM, who
said: "I have a problem for you to solve." SIMON replied, "tell me more
about your problem," and walked on.

===================================================================

from <[email-masked]>

How long would a simulation of its own lifetime survive?

What is the rate of change of all metaphors for the viability of that rate?

===================================================================

someone resent me Gabriel's old '83 koan <robins@usc-isib>:

A famous Lisp Hacker noticed an Undergraduate sitting in front of a
Xerox 1108, trying to edit a complex Klone network via a browser.
Wanting to help, the Hacker clicked one of the nodes in the network
with the mouse, and asked "what do you see?"  Very earnesty, the
Undergraduate replied "I see a cursor." The Hacker then quickly pressed
the boot toggle at the back of the keyboard, while simultaneously
hitting the Undergraduate over the head with a thick Interlisp Manual.
The Undergraduate was then Enlightened.

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

Date: Wed, 19 Feb 86 14:38 PST
From: Cottrell@NPRDC
Subject: The Naive Dog Physics Manifesto

From: Leslie Kaelbling <[email-masked]>
From: [email-masked]
From: [email-masked] (Charles Haynes)

                                     SEMINAR

                           From PDP to NDP through LFG:
                         The Naive Dog Physics Manifesto

                               Garrison W. Cottrell
                            Department of Dog Science
               Condominium Community College of Southern California

          The Naive Physics Manifesto (Hayes, 1978) was a  seminal  paper  in
     extending  the theory of knowledge representation to everyday phenomena.
     The goal of the present work is to extend this approach to Dog  Physics,
     using  the  connectionist  (or  PDP)  framework  to encode our everyday,
     commonsense knowledge about  dogs  in  a  neural  network[1].   However,
     following Hayes, the goal is not a working computer program.  That is in
     the province of so-called performance theories of Dog Physics (see,  for
     example,  my  1984  Modelling the Intentional Behavior of the Dog). Such
     efforts are bound to fail, since they must correspond to empirical data,
     which  is  always  changing.   Rather,  we  will  first  try to design a
     competence theory of dog physics[2], and, as with Hayes and Chomsky, the
     strategy  is  to  continually  refine  that, without ever getting to the
     performance theory.

          The approach taken here is to develop a  syntactic  theory  of  dog
     actions  which  is  constrained  by  Dog  Physics.   Using  a variant of
     Bresnan's Lexical-Functional Grammar,  our  representation  will  be  an
     context-free  action  grammar,  with  associated s-structures (situation
     structures).   The  s-structures  are  defined  in  terms  of  Situation
     Dogmatics[3],  and  are  a partial specification of the situation of the
     dog during that action.

          Here  is  a  sample  grammar  which  generates  strings  of  action
     predicates corresponding to dog days[4], (nonterminals are capitalized):

        Day -> Action Day | Sleep

        Action -> Sleep | Eat | Play | leavecondo Walk

        Sleep -> dream Sleep | deaddog Sleep | wake

        Eat -> Eat chomp | chomp

        Play -> stuff(Toy, mouth) | hump(x,y) | getpetted(x,y)

        Toy -> ball | sock

        Walk -> poop Walk | trot Walk | sniff Walk | entercondo

          Several regularities are captured  by  the  syntax.   For  example,
     these  rules  have  the  desirable property that pooping in the condo is
     ungrammatical.  Obviously such grammatical details are not innate in the
     infant  dog.   This  brings  us  to the question of rule acquisition and
     Universality.  These context-free action rules are assumed to be learned
     by a neural network with "hidden" units[5] using  the  bark  propagation
     method (see Rumelhart & McClelland, 1985; Cottrell 1985).  The beauty of
     this is that  Dogmatic  Universality  is  achieved  by  assuming  neural
     networks to be innate[6].

          The above rules generate some impossible sequences, however.   This
     is  the  job of the situation equation annotations.  Some situations are
     impossible, and this acts as a filter on  the  generated  strings.   For
     example, an infinite string of stuff(Toy, mouth)'s are prohibited by the
     constraint that the situated dog can only fit one ball and one  sock  in
     her mouth at the same time.  One of the goals of Naive Dog Physics is to
     determine these commonsense constraints.  One of our  major  results  is
     the  discovery  that  dog  force  (df)  is  constant.  Since df = mass *
     acceleration, this means that smaller dogs accelerate faster,  and  dogs
     at rest have infinite mass.  This is intuitively appealing, and has been
     borne out by my dogs.
     ____________________
        [1]We have decided not to use FOPC, as this has been proven by Schank
     (personal communication) to be inadequate, in a proof too loud to fit in
     this footnote.
        [2]The use of competence theories is a standard  trick  first  intro-
     duced  by  Chomsky, which avoids the intrusion of reality on the theory.
     An example is Chomsky's theory of light bulb changing, which  begins  by
     rotating the ceiling...
        [3]Barwoof & Peppy (1983).  Situation Dogmatics (SD) can be  regarded
     as a competence theory of reality. See previous footnote.  Using SD is a
     departure from Hayes, who exhorts us to "understand what [the  represen-
     tation]  means." In the Gibsonian world of Situation Dogmatics, we don't
     know what the representation means.  That would  entail  information  in
     our  heads.  Rather, following B&P, the information is out there, in the
     dog. Thus, for example, the dog's bark means there are  surfers  walking
     behind the condo.
        [4]Of course, a less ambitious approach would just try to account for
     dog day afternoons.
        [5]It is never clear in these models where these units are hidden, or
     who hid them there. The important thing is that you can't see them.
        [6]Actually  this  assumption  may  be too strong when applied to the
     dogs under consideration. However, this is much weaker than Pinker's as-
     sumption  that  the  entirety  of  Joan  Bresnan's mind is innate in the
     language learner.  It is instructive to see how  his  rules  would  work
     here.   We  assume hump(x,y) is innate, and x is bound by the default s-
     function "Self".  The first time  the  puppy  is  humped,  the  mismatch
     causes  a  new  Passive  humping entry to be formed, with the associated
     redundancy rule. Evidence for the generalization to other predicates  is
     seen in the puppy subsequently trying to stuff her mouth into the ball.

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

End of AIList Digest
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