Copy Link
Add to Bookmark
Report
AIList Digest Volume 2 Issue 123
AIList Digest Sunday, 23 Sep 1984 Volume 2 : Issue 123
Today's Topics:
AI Tools - OPS5,
Expert Systems - Computer Program Usage Consultant,
Literature - Introductory Books & IEEE Computer Articles,
LISP - VMS LISPS,
Logic - Induction and Deduction & Causality,
Humor - Slimy Logic Seminar,
Seminar - Analysis of Knowledge,
Course & Conference - Stanford Logic Meeting
----------------------------------------------------------------------
Date: 21 Sep 84 13:24:47 EDT
From: BIESEL@RUTGERS.ARPA
Subject: Info needed on OPS5
Any information on compilers/interpreters for the OPS5 language on VAXen
will be appreciated. I'm particularly interested in relatively short
reviews and/or introductions to the language; a tutorial would be nice.
If any of this stuff is available online I'd like to FTP it.
Thanx in advance.
Biesel@rutgers.arpa
------------------------------
Date: 17 Sep 84 15:28:05-PDT (Mon)
From: hplabs!tektronix!uw-beaver!ssc-vax!alcmist @ Ucb-Vax.arpa
Subject: Computer Program Usage Consultants?
Article-I.D.: ssc-vax.99
I am working on an expert system to advise users setting up
runs of a complex aerodynamics program. The project is sort of like
SACON, only we're trying to do more.
Does anyone know of work in progress that I should know about? I
am interested in any work being done on
1. Helping users set up appropriate inputs for a
sophisticated analytical or simulation program,
2. Diagnosing problems with the output of such a program,
or
3. Interpreting large volumes of numerical output in
a knowledgeable fashion.
I am looking for current work that people are willing to talk about.
Pointers to literature will be appreciated, even though our library
is doing a literature search.
Please reply by mail! I will send a summary of responses to anybody
who wants one.
Fred Wamsley
Boeing Computer Services AI Center
UUCP: {decvax,ihnp4,sdcsvax,tektronix}!uw-beaver!ssc-vax!alcmist
ARPA: ssc-vax!alcmist@uw-beaver.ARPA
------------------------------
Date: 15 Sep 84 10:38:00-PDT (Sat)
From: pur-ee!uiucdcs!convex!graham @ Ucb-Vax.arpa
Subject: "introductory" book on AI??
Article-I.D.: convex.45200003
I would like to learn more about the AI field. I am almost "illiterate" now.
I have a PhD in CS from Illinois and 26 years experience in system software
such as compilers, assemblers, link-editors, loaders, etc... Can anyone cite
a good book or books for the AI field which
is comprehensive
is tutorial, in the sense that it includes the motivation behind
the avenues in AI that it describes, and
includes a good bibliography to other works in the field?
[Previous AIList discussion on this subject seems to have found Winston's
new "Artificial Intelligence" and Elaine Rich's "Artificial Intelligence"
to be good textbooks. The three-volume Handbook of AI is also excellent.
Older texts by Nils Nilsson and by Bertram Raphael ("The Thinking Computer")
still have much to offer. Other recent books cover LISP, PROLOG, and AI
programming techniques, as well as expert systems and AI as a business.
-- KIL]
------------------------------
Date: Fri 21 Sep 84 10:08:00-PDT
From: Ken Laws <Laws@SRI-AI.ARPA>
Subject: Knowledge Engineering Article
The September issue of IEEE Computer is devoted to AI systems, with
emphasis on the man-machine interface. It's well worth reading.
Frederick Hayes-Roth's article seems to be an excellent introduction
to knowledge engineering. (The title is "The Knowledge-Based Expert
System: A Tutorial," but it is not really an expert-systems overview.)
The article by Elaine Rich on natural-language interfaces is also
excellent. There are other articles on smart databases, tutoring
systems, job-shop control, and decision support systems.
There is also an article on a declarative parameter-specification
system for Schlumberger's Crystal system. I found the article hard
to follow, and I have strong doubts about the desirability of building
a domain-independent parameter parser, then using procedural attachment
in the parameter declarations to hack in runtime dependencies and
domain-specific intelligent behavior. Even if this is to be done,
the base program should have the option of requesting parameters only
as (and if) they are needed, and should be able to create or alter the
declarative structures dynamically at the time the parameters are
requested. Given such a system, the declarative structures are simple
a convenient way of passing control options to the user-query
subroutine. Most of the procedual knowledge belong in the procedural
code, not in declarative structures in a separate knowledge base.
-- Ken Laws
------------------------------
Date: Sat, 22 Sep 84 14:48:59 EDT
From: Gregory Parkinson <Parkinson@YALE.ARPA>
Subject: VMS LISPS
We run Yale's T on VMS and like it a lot. According to our benchmarks
it runs (on the average) a little faster than DEC's Common Lisp. The
T compiler gets rid of tail recursion which speeds things up a bit, and
is about 40 times faster when dealing with labels. Subjectively, working
with CL after working with T feels like driving a 76 Caddie Eldorado (power
windows, seats, brakes, steering, etc.) after getting used to a Honda CRX.
They both get you where you're going, but there's something about the
Honda that makes you feel like you're really driving......
Greg Parkinson
Cognitive Systems, inc.
------------------------------
Date: 21 Sep 84 3:42:03-EDT (Fri)
From: hplabs!hao!seismo!mcvax!vu44!tjalk!dick @ Ucb-Vax.arpa
Subject: Proof by induction, fun & entertainment
Article-I.D.: tjalk.338
Claim: All elements of an array A[1..n] are equal to its first element.
Proof by induction:
Starting case: n = 1.
Proof:
Obvious, since A[1] = A[1].
Induction step:
If the Claim is true for n = N, it is true for n = N + 1.
Proof:
All elements of A[1..N] are equal (premise), and since
A[2..N+1] is an array of length N, all its elements
are equal also. A[N] is in both (sub-)arrays, so
A[1] = A[N] and
A[N] = A[N+1] ->
A[1] = A[N+1]
which makes all of A[1..N+1] equal.
End of proof of induction step
The starting case and the induction step together prove the Claim.
End of proof by induction
Courtesy of Dick Grune
Vrije Universiteit
Amsterdam
the Netherlands
[ *** Spoiler *** The flaw, of course, is in the statement that
"A[N] is in both (sub-)arrays". (I point this out to avoid a flood of
mail supplying the answer.) -- KIL]
------------------------------
Date: Fri, 21 Sep 84 08:36 CDT
From: Boebert@HI-MULTICS.ARPA
Subject: More on induction and deduction
More on induction and deduction, along with much other interesting and
entertaining discussion, can be found in
Proofs and Refutations
by Imre Lakatos
Cambridge
------------------------------
Date: Fri 21 Sep 84 10:32:39-PDT
From: BARNARD@SRI-AI.ARPA
Subject: induction vs. deduction
In reply to the claim that my statement
'deduction proceeds from the general (axioms) to
the specific (propositions), induction proceeds from
the specific to the general.'
is not correct (according to Kahane, LOGIC AND CONTEMPORARY RHETORIC),
see Aristotle, BASIC WORKS OF ARISTOTLE, ed. by R. McKeon, Random
House, 1941.
------------------------------
Date: 18 Sep 84 5:54:04-PDT (Tue)
From: hplabs!hao!seismo!umcp-cs!chris @ Ucb-Vax.arpa
Subject: Re: Causality
Article-I.D.: umcp-cs.16
(Apply :-) to entire reply)
> What's wrong with event A affecting event B in event A's past? You
>can't go back and shoot your own mother before you were born because you
>exist, and obviously you failed. If we assume the universe is
>consistant [and not random chaos], then we must assume inconsistancies
>(such as shooting your own mother) will not arise. It does not,
>however, place time constrictions on cause and effect.
Who says you can't even do that? Perhaps your existence is actually
just a probablility function. If P(existence) becomes small enough
you'll just disappear. Maybe that explains all those mysterious
disappearances (``He just walked around the horses a moment ago...'')
In-Real-Life: Chris Torek, Univ of MD Comp Sci (301) 454-7690
UUCP: {seismo,allegra,brl-bmd}!umcp-cs!chris
CSNet: chris@umcp-cs ARPA: chris@maryland
------------------------------
Date: 17 Sep 84 18:21:16-PDT (Mon)
From: hplabs!hpda!fortune!wdl1!jbn @ Ucb-Vax.arpa
Subject: Re: Now and Then
Article-I.D.: wdl1.424
Having spent some years working on automatic theorem proving and
program verification, I am occasionally distressed to see the ways in which
the AI community uses (and abuses) formal logic. Always bear in mind that
for a deductive system to generate only true statements, the axioms of the
system must not imply a contradiction; in other words, it must be impossible
to deduce TRUE = FALSE. In a system with a contradiction, any statement,
however meaningless, can be generated by deductive means.
It is difficult to ensure the soundness of one's axioms. See Boyer
and Moore's ``A Computational Logic'' for a description of a logic for which
soundness can be demonstrated and a program which generates inductive proofs
based on that logic. The Boyer and Moore approach works only for mathematical
objects constructed in a specific and rigorous manner. It is not applicable
to ``real world reasoning.''
There are schemes such as nonmonotonic reasoning which attempt to deal
with contradictions. These are not logical systems but heuristic systems.
Some risk of incorrect results is accepted in exchange for the ability to
``reason'' with non-rigorous data. A clear distinction should be made between
mathematical deduction in rigorous spaces and heuristic problem solving by
semi-logical means.
John Nagle
------------------------------
Date: 20 Sep 1984 10:44 EDT (Thu)
From: Walter Hamscher <WALTER%MIT-OZ@MIT-MC.ARPA>
Subject: Humor & Seminar - Slimy Logic
[Forwarded from the MIT bboard by SASW@MIT-MC.]
The Computer Aided Conceptual Art Laboratory
and
Laboratory for Graduate Student Lunch
presents
SLIMY LOGIC
or
INDENUMERABLY MANY TRUTH-VALUED LOGIC WITHOUT HAIR
by Lofty Zofty
The indenumerably many-valued logics which result from the first stage
of slime-ification are so to speak "non-standard" logics; but slimy logic,
the result of the second stage of slime-ification, is a very radical
departure indeed from classical logics, and thereby sidesteps many
fruitless preoccupations of logicians such as completeness, consistency,
axiomatization, and proof. In this talk I attempt to counter Slimy Logic's
low and ever-declining popularity by presenting a "qualitative" view
of slimy logic in which such definitions as
2
very true = true
and -3/2
not very pretty false = false
by the qualitative (i.e. so even people who don't carry
around two calculators can understand them) definitions:
very true = true
and
not very pretty false = ugly false
I will then use this "qualitative" slimy logic to very nearly prove
very much that Jon Doyle is probably not very right about nearly
extremely many things.
HOSTS: Robert Granville and Isaac Kohane
Refreshments will be served
Moved to the Third Floor Theory Group Playroom
------------------------------
Date: 20 September 1984 13:30-EDT
From: Kenneth Byrd Story <STORY @ MIT-MC>
Subject: Seminar - Analysis of Knowledge
[Forwarded from the MIT bboard by SASW@MIT-MC.]
DATE: Wednesday, September 26, 1984
TIME: Refreshments, 3:45pm
Lecture, 4:00pm
PLACE: NE43-453
TITLE: ``A MODEL-THEORETIC ANALYSIS OF KNOWLEDGE''
SPEAKER: Dr. Joseph Y. Halpern, IBM, San Jose
Understanding knowledge is a fundamental issue in many disciplines. In
computer science, knowledge arises not only in the obvious contexts (such as
knowledge-based systems), but also in distributed systems (where the goal is to
have each processor know something, as in Byzantine agreement). A general
semantic model of knowledge is introduced, to allow reasoning about statements
such as "He knows that I know whether or not she knows whether or not it is
raining." This approach more naturally models a state of knowledge than
previous proposals (including Kripke structures). Using this notion of model,
a model theory for knowledge is developed. This theory enables one to
interpret such notions as a "finite amount of information" and "common
knowledge" in different contexts. This is joint work with Ron Fagin and Moshe
Vardi.
HOST: Professor Silvio Micali
------------------------------
Date: Mon 17 Sep 84 09:01:21-PDT
From: Jon Barwise <BARWISE@SU-CSLI.ARPA>
Subject: Course & Conference - Stanford Logic Meeting
Logic, Language and Computation Meeting
The Association for Symbolic Logic (ASL) and the Center for the Study
of Language and Information (CSLI) are planning a two-week summer
school and meeting, July 8-20, 1985, at Stanford University. The
first week (July 8-13) will consist of a CSLI Summer School, with
courses on various topics, including PROLOG, LISP, Complexity Theory,
Denotational Semantics, Generalized Quantifiers, Intensional Logic,
and Situation Semantics. The second week (July 15-20) will be an ASL
meeting with invited lectures (in Logic, Natural Language, and
Computation), symposia (on "Logic in Artificial Intelligence", "Types
in the Study of Computer and Natural Languages", and "Possible
Worlds"), and sessions for contributed papers. Those interested
should contact Ingrid Deiwiks, CSLI, Ventura Hall, Stanford, CA 94305
(ph 415-497-3084) before November 1, with an indication as to whether
they would like to make a reservation for a single or shared room and
board in a residence hall, and for what period of time. A more
detailed program will be available in November. The program committee
consists of Jon Barwise, Solomon Feferman, David Israel and William
Marsh.
------------------------------
End of AIList Digest
********************
