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AIList Digest Volume 2 Issue 065
AIList Digest Monday, 28 May 1984 Volume 2 : Issue 65
Today's Topics:
AI Tools - KS300 & MicroPROLOG and LISP,
Expert Systems - Checking of NMOS Cells,
AI Courses - Expert Systems,
Cognition - Dreams & ESP,
Seminars - Explanation-Based Learning & Analogy in Legal Reasoning &
Nonmonotonicity in Information Systems
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Date: 23 May 84 12:42:27-PDT (Wed)
From: hplabs!hao!seismo!cmcl2!philabs!linus!vaxine!chb @ Ucb-Vax
Subject: KS300 Question
Article-I.D.: vaxine.266
Does anybody know who owns the rights to the KS300 expert systems tool?
KS300 is an EMYCIN lookalike, and I think it runs under INTERLISP. Any help
would be appreciated.
-----------------------------------------------------------
"It's not what you look like when you're doin' what you're doin', it's what
you're doin' when you're doin' what you look like what you're doin'"
---125th St. Watts Band
Charlie Berg
...allegra!vaxine!chb
------------------------------
Date: 25 May 84 12:28:22-PDT (Fri)
From: hplabs!hao!seismo!cmcl2!floyd!whuxle!spuxll!abnjh!cbspt002 @
Ucb-Vax
Subject: MicroPROLOG and LISP for the Rainbow?
Article-I.D.: abnjh.647
Can anybody point me toward microPROLOG and LISPs for the DEC
Rainbow 100. Either CP/M86 or MS-DOS 2.0, 256K, floppies.
Thanks in advance.
M. Kenig
ATT-IS, S. Plainfield NJ
uucp: ...!abnjh!cbspt002
------------------------------
Date: 25 May 1984 1438-PDT (Friday)
From: cliff%ucbic@Berkeley (Cliff Lob)
Subject: request for info
This is a request to hear about any work that is going on
related to my master's research in expert systems:
RULE BASE ERROR CHECKING OF NMOS CELLS
The idea is to build an expert system that embodies the knowledge
of expert VLSI circuit designers to criticize NMOS circuit design
at the cell (<15 transistors) level. It is not to be a simulator,
but rather it is to be used by designers to have their cell critiqued
by an experienced expert. The program will be used to try to catch
the subtle bugs (ie non-logic error, not shown by standard simulation)
that occur in the cell design process.
I will be writing the code in PSL and a KRL Frame type language.
Is there any work of a similar nature going on?
Cliff Lob
cliff@ucbic.BERKELEY
------------------------------
Date: Fri 25 May 84 13:33:49-MDT
From: Robert R. Kessler <KESSLER@UTAH-20.ARPA>
Subject: re: Expert systems course (Vol 2, #64)
I taught a course this spring quarter on "Knowledge Engineering" using the
Hayes-Roth text. Since we only had a quarter, I decided to focus on
writing expert systems as opposed to developing expert systems tools. We
had available Hewlett Packard's Heuristic Programming and Representation
Language (HPRL) to use to build some expert systems. A general outline
follows:
First third: Covered the first 2 to 3 chapters of the text.
This gave the students enough exposure to general expert systems
concepts.
Second third: In depth exposure of HPRL. Studied knowledge
representation using their Frame structure and both forward and
backward chaining rules.
Final third: Discussed the Oak Ridge Natl Lab problem covered in Chapter
10 of the text. We then went through each of the systems described
(Chapters 6 and 9) to understand their features and misfeatures.
Finally, we contrasted how we would have solved the problem using
HPRL.
Students had various assignments during the first half of the quarter to
learn about frames, and both types of rules. They then (and are right
now) working on a final expert system of their own choosing (have varied
from a mechanics helper, plant doctor, first aid expert, simulator of the
SAIL game, to others).
All in all, the text was very good, and is so far the best I've seen.
Bob.
------------------------------
Date: Sat, 26 May 84 17:06:57 PDT
From: Philip Kahn <kahn@UCLA-CS.ARPA>
RE: Subject: cognitive psychology / are dreams written by a committee?
FLAME ON
Where can you find any evidence that "dreams are programmed,
scheduled event-sequences, not mere random association?"
I have never found any author that espoused this viewpoint.
Per chance, I think that viewpoint imposes far too much conscious
behavior onto unconscious phenomena? If they are indeed run by
a "committee", what happens during a proxy fight?
FLAME OFF
------------------------------
Date: Fri 25 May 84 10:13:51-PDT
From: NETSW.MARK@USC-ECLB.ARPA
Subject: epiphenomenon conjecture
conjecture: 'consciousness', 'essence' etc. are epiphenomena at the
level of the 'integrative function' which facilitates the interaction
between members of the 'community' of brain-subsystems. Many a-i
systems have been developed which model particular putative or likely
brain-subsystems, what is the status of efforts allowing the integration
of such systems in an attempt to model the consciousness as a
'community of a-i systems' ???
------------------------------
Date: Fri, 25 May 84 10:09:44 PDT
From: Scott Turner <srt@UCLA-CS.ARPA>
Subject: Dreams...Far Out
Did the astronauts on the moon suffer any problems with dreams, etc? Without
figuring the attentuation, it seems like that might be far enough away to
cause problems with reception...since I don't recall any such effects, perhaps
we can assume that mankind doesn't have any such carrier wave.
Makes a good base for speculative fiction, though. Interstellar travel
would have to be done in ships large enough to carry a critical mass of
humans. Perhaps insane people are merely unable to pick up the carrier wave,
and so on.
-- Scott
------------------------------
Date: Sun 27 May 84 11:44:43-PDT
From: Joe Karnicky
Reply-to: ZZZ.V5@SU-SCORE.ARPA
Subject: Re: existence of telepathy
I disagree strongly with Ken's assertion that "There seems to be growing
evidence that telepathy works, at least for some people some of the time."
(May 21 AIlist). It seems to me that the evidence which exists now is the
same as has existed for possibly 100,000 years, namely anecdotes and poorly
controlled experiments. I recommend reading the book "Science: Good, Bad,
and Bogus" by Martin Gardner, or any issue of "The Skeptical Observer".
What do you think ?
Joe Karnicky
------------------------------
Date: 23 Apr 84 10:51:01 EST
From: DSMITH@RUTGERS.ARPA
Subject: Seminar - Explanation-Based Learning
[This and the following Rutgers seminar notices were delayed because
I have not had access to the Rutgers bboard for several weeks. This
seems a good time to remind readers that AIList carries such abstracts
not to drum up attendance, but to inform those who cannot attend. I
have been asked several times for help in contacting speakers, evidence
that the seminar notices do prompt professional interchanges. -- KIL]
Department of Computer Science
COLLOQUIUM
SPEAKER: Prof. Gerald DeJong
University of Illinois
TITLE: EXPLANATION BASED LEARNING
Machine Learning is one of the most important current areas of Artificial
Intelligence. With the trend away from "weak methods" and toward a more
knowledge-intensive approach to intelligence, the lack of knowledge in an
Artificial Intelligence system becomes one of the most serious limitations.
This talk advances a technique called explanation based learning. It is a
method of learning from observations. Basically, it involves endowing a system
with sufficient knowledge so that intelligent planning behavior of others can
be recognized. Once recognized, these observed plans are generalized as far as
possible while preserving the underlying explantion of their success. The
approach supports one-trial learning. We are applying the approach to three
diverse areas: Natural Language processing, robot task planning, and proof of
propositional calculus theorems. The approach holds promise for solving the
knowedge collection bottleneck in the construction of Expert Systems.
DATE: April 24
TIME: 2:50 pm
PLACE: Hill 705
Coffee at 2:30
Department of Computer Science
COLLOQUIUM
SPEAKER: Rishiyur Nikhil
University of Pennsylvania
TITLE: FUNCTIONAL PROGRAMMING LANGUAGES AND DATABASES
ABSTRACT
Databases and Programming Languages have traditionally been "separate"
entities, and their interface (via subroutine libraries, preprocessors, etc.)
is generally cumbersome and error-prone.
We argue that a functional programming language, together with a data model
called the "Functional Data Model", can provide an elegant and simple
integrated database programming environment. Not only does the Functional Data
Model provide a richer model for new database systems, but it is also easy to
implement atop existing relational and network databases. A "combinator"-style
implementation technique is particularly suited to implementing a functional
language in a database environment.
Functional database languages also admit a rich type structure, based on
that of the programming language ML. While having the advantages of strong
static type-checking, and allowing the definition of user-views of the
database, it is unobtrusive enough to permit an interactive, incremental,
Lisp-like programming style.
We shall illustrate these ideas with examples from the language FQL, where
they have been prototyped.
DATE: Thursday, April 26, 1984
TIME: 2:50 p.m.
PLACE: Room 705 - Hill Center
Coffee at 2:30
------------------------------
Date: 3 May 84 16:21:34 EDT
From: Michael Sims <MSIMS@RUTGERS.ARPA>
Subject: Seminar - Analogy in Legal Reasoning
[Forwarded from the Rutgers bboard by Laws@SRI-AI.]
machine learning brown bag seminar
Title: Analogy with Purpose in Legal Reasoning from Precedents
Speaker: Smadar Kedar-Cabelli <Kedar-Cabelli@Rutgers.Arpa>
Date: Wednesday, May 9, 1984, 12:00-1:30
Location: Hill Center, Room 423 (note new location)
One open problem in current artificial intelligence (AI) models of
learning and reasoning by analogy is: which aspects of the analogous
situations are relevant to the analogy, and which are irrelevant? It
is currently recognized that analogy involves mapping some underlying
causal structure between situations [Winston, Gentner,
Burstein,Carbonell]. However, most current models of analogy provide
the system with exactly the relevant structure, tailor-made to each
analogy to be performed. As AI systems become more complex, we will
have to provide them with the capability of automatically focusing on
the relevant aspects of situations when reasoning analogically. These
will have to be sifted from the large amount of information used to
represent complex, real-world situations.
In order to study these general issues, I am examining a particular
case study of learning and reasoning by analogy: legal reasoning from
precedents. This is studied within the TAXMAN II project, which is
investigating legal reasoning using AI techniques [McCarty, Sridharan,
Nagel].
In this talk, I will discuss the problem and a proposed solution. I
am examining legal reasoning from precedents within the context of
current AI models of analogy. I plan to add a focusing capability.
Current work on goal-directed learning [Mitchell, Keller] and
explanation-based learning [DeJong] applies here: the explanation of
how a the analogous precedent case satisfies the goal of the legal
argument helps to automatically focus the reasoning on what is
relevant.
Intuitively, if your purpose is to argue that a certain stock
distribution is taxable by analogy to a precedent case, you will know
that aspects of the cases having to do with the change in the economic
position of the defendants are relevant for the purpose of this
analogy, while aspects of the case such as the size of paper on which
the stocks were printed, or the defendants' hair color, are irrelevant
for that purpose. This knowledge of purpose, and the ability to use it
to focus on relevant features, are missing from most current AI models
of analogy.
------------------------------
Date: 15 May 84 11:13:50 EDT
From: BORGIDA@RUTGERS.ARPA
Subject: Seminar - Nonmonotonicity in Information Systems
[Forwarded from the Rutgers bboard by Laws@SRI-AI.]
III Seminar by Alex Borgida, Wed. 2:30 pm/Hill 423
The problem of Exceptional Situations in Information Systems --
An overview
We begin by illustrating the wide range of exceptional situations which can
arise in the context of Information Systems (ISs). Based on this evidence, we
argue for 1) a methodology of software design which abstracts
exceptional/special cases by considering normal cases first and introducing
special cases as annotations in successive phases of refinement, and 2) the
need for ACCOMMODATING AT RUN TIME exceptional situations not anticipated
during design. We then present some Programming Language features which we
believe support the above goals, and hence facilitate the design of more
flexible ISs.
We conclude by briefly describing two research issues in Artificial
Intelligence which arise out of this work: a) the problem of logical reasoning
in a knowledge base of formulas where exceptions "contradict" general rules,
and b) the issue of suggesting improvements to the design of an IS based on the
exceptions to it which have been encountered.
------------------------------
End of AIList Digest
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