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AIList Digest Volume 5 Issue 021
AIList Digest Thursday, 29 Jan 1987 Volume 5 : Issue 21
Today's Topics:
Queries - Learning Programs & CMU's "GRAPES" &
1987 Society for Computer Simulation Multiconference,
Seminars - Circumscriptive Query Answering (SU) &
Automation in Seismic Interpretation (SMU) &
A Four-Valued Semantics for Terminological Logics (AT&T) &
Learning when Irrelevant Variables Abound (IBM),
Conference - AI and Law
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Date: 26 Jan 87 17:36:23 GMT
From: carlson@lll-tis-b.arpa (John Carlson)
Subject: Learning programs wanted [Public Domain preferred]
Can anyone give me pointers to programs that learn? In
particular, does anyone have an copy of the "Marvin"
program that appeared in Byte a couple of months ago?
John Carlson
--
INTERNET carlson@lll-tis-b.ARPA
UUCP ...lll-crg!styx!carlson
------------------------------
Date: 27 Jan 87 20:12:07 GMT
From: gatech!mcnc!rti-sel!hlw@hplabs.hp.com (Hal Waters)
Subject: Info wanted on CMU's "GRAPES"
I wish to get information on CMU's "GRAPES".
Specifically, who wrote the software?
Is it a tool/shell for creating Intelligent Tutoring Systems?
Is it a Cognitive Simulation Model?
Is it Public Domain?
If so, or if not, how can I get a copy of the software?
Please mail responses to me at hlw@rti-sel.
Thanks in advance!
Hal Waters
------------------------------
Date: 27 Jan 87 11:48:00 EST
From: "MATHER, MICHAEL" <mather@ari-hq1.ARPA>
Reply-to: "MATHER, MICHAEL" <mather@ari-hq1.ARPA>
Subject: 1987 Society for Computer Simulation Multiconference
Information on the 1987 Society for Computer Simulation Multiconference was
published in the last AIList. Does anyone know if this conference has already
taken place or, if not, when and where will it take place?
------------------------------
Date: 26 Jan 87 1434 PST
From: Vladimir Lifschitz <VAL@SAIL.STANFORD.EDU>
Subject: Seminar - Circumscriptive Query Answering (SU)
Commonsense and Nonmonotonic Reasoning Seminar
A QUERY ANSWERING ALGORITHM
FOR CIRCUMSCRIPTIVE AND CLOSED-WORLD THEORIES
Teodor C. Przymusinski
University of Texas at El Paso
<ft00@utep.bitnet>
Thursday, January 29, 4pm
Bldg. 160, Room 161K
McCarthy's theory of circumscription appears to be the most powerful
among various non-monotonic logics designed to handle incomplete and
negative information in knowledge representation systems. In this
presentation we will describe a query answering algorithm for
circumscriptive theories.
The algorithm is based on a modified version of ordered linear resolution
(OL-resolution), which we call a MInimal model Linear Ordered resolution
(MILO-resolution). MILO-resolution constitutes a sound and complete procedure
to determine the existence of minimal models satisfying a given formula.
Our algorithm is the first query evaluation algorithm for general
circumscriptive theories. The Closed-World Assumption (CWA) and its
generalizations, the Generalized Closed-World Assumption (GCWA) and
the Extended Closed-World Assumption (ECWA), can be considered as
special forms of circumscription. Consequently, our algorithm also
applies to answering queries in theories using the Closed-World Assumption
and its generalizations. Similarly, since prioritized circumscription
is equivalent to a conjunction of (parallel) circumscriptions, the
algorithm can be used to answer queries in theories circumscribed by
prioritized circumscription.
------------------------------
Date: WED, 10 oct 86 17:02:23 CDT
From: leff%smu@csnet-relay
Subject: Seminar - Automation in Seismic Interpretation (SMU)
January 28, 1987, 1:30PM, 315SIC Computer Science Department,
Southern Methodist Univeristy, Dallas, Texas
AUTOMATION IN SEISMIC INTERPRETATION
Bruce Flinchbaugh
Texas Instruments
ABSTRACT
Interpreting three-dimensional seismic data is important for oil and
gas exploration. Part of the problem is perception-intensive (experts
spend much of their time looking at the data), and part of the problem
is more cognition-intensive (experts reconcile perceived structures
with knowledge of plausible geology and other sources of information).
This talk will present a simple overview of the seismic data
acquisition and processing required to produce three-dimensional
seismic data volumes. Then a variety of tools for assisting in the
interpretation of the data will be discussed. For the most part
today's useful tools are aimed at solving the perception-intensive
problems. Finally some open problems in seismic interpretation will
be described.
BIOGRAPHY
Dr. Flinchbaugh is a Senior Member of Technical Staff at T.I. in the
Computer Science Center Artificial Intelligence Laboratory, where he
is currently tackling semiconductor manufacturing automation problems.
Also at T.I. he has invented techniques assisting in the processing
and structural interpretation of three-dimensional seismic data.
Previous research in artificial intelligence, at M.I.T. and The Ohio
State University, addressed computational vision problems involving
the interpretation of motion and color. Dr. Flinchbaugh received his
Ph.D. in computer and information science from The Ohio State
University in 1980.
------------------------------
Date: Thu, 22 Jan 87 10:46:34 est
From: allegra!dlm
Subject: Seminar - A Four-Valued Semantics for Terminological Logics
(AT&T)
[Forwarded from the NL-KR Digest.]
Title: A Four-Valued Semantics for Terminological Logics
Speaker: Peter F. Patel-Schneider
Affiliation: Schlumberger Palo Alto Research
Date: Monday, February 2, 1987
Location: AT&T Bell Laboratories - Murray Hill 3D-473
Sponsor: Ron Brachman
Terminological logics (also called frame-based description languages)
are a clarification and formalization of some of the ideas underlying
semantic networks and frame-based systems. The fundamental
relationship in these logics is whether one concept (frame, class) is
more general than (subsumes) another. This relationship forms the
basis for important operations, including recognition, classification,
and realization, in knowledge representation systems incorporating
terminological logics.
However, determining subsumption is computationally intractable under
the standard semantics for terminological logics, even for languages
of very limited expressive power. Several partial solutions to this
problem are used in knowledge representation systems, such as NIKL,
that incorporate terminological logics, but none of these solutions
are satisfactory if the system is to be of general use in representing
knowledge.
A new solution to this problem is to use a weaker, four-valued
semantics for terminological logics, thus legitimizing a smaller set
of subsumption relationships. In this way a computationally tractable
knowledge representation system incorporating a more expressively
powerful terminological logic can be built.
------------------------------
Date: Tue 27 Jan 87 20:05:30-PST
From: Ramsey Haddad <HADDAD@Sushi.Stanford.EDU>
Subject: Seminar - Learning when Irrelevant Variables Abound (IBM)
Next BATS will be at IBM Almaden Research Center on Friday, February 13.
Following is a preliminary schedule:
9:45 - 10:00 Coffee + +
10:00 - 11:00 "Algebraic Methods in the Theory of Lower Bounds
for Boolean Circuit Complexity"
Norman Smolensky, U.C. Berkeley.
11:00 - 12:00 " The Decision Problem for the Probabilities
of Higher-Order Properties".
Phokion Kolaitis, IBM Almaden.
1:00 - 2:00 "Learning When Irrelevant Features Abound"
Nick Littlestone, U.C. Santa Cruz.
2:00 - 3:00 "Fast Parallel Algorithms for Chordal Graphs"
Alejandro A. Schaffer, Stanford University.
===================================================================
"Learning When Irrelevant Features Abound"
Nick Littlestone
U.C. Santa Cruz
Valiant and others have studied the problem of learning various classes
of Boolean functions from examples. Here we discuss on-line learning of
these functions. In on-line learning, the learner responds to each
example according to a current hypothesis. Then the learner updates the
hypothesis, if necessary, based on the correct classification of the
example. This is the form of the Perceptron learning algorithms, in
which updates to the weights occur after each mistake. One natural
measure of the quality of learning in the on-line setting is the number
of mistakes the learner makes. For suitable classes of functions,
on-line learning algorithms are available which make a bounded number of
mistakes, with the bound independent of the number of examples seen by
the learner. We present one such algorithm, which learns disjunctive
Boolean functions. The algorithm can be expressed as a linear-threshold
algorithm. If the examples include a large number of irrelevant
variables, the algorithm does very well, the number of mistakes
depending only logarithmically on the number of irrelevant variables.
More specifically, if the function being learned is of the form $f ( x
sub 1 ,..., x sub n )~=~x sub {i sub 1} orsign ... orsign x sub {i sub
k} then the mistake bound is $O ( k log n )$. If $k = O ( log n )$ then
this bound is significantly better than that given by the Perceptron
convergence theorem.
------------------------------
Date: 8 Jan 87 14:30:33 EST
From: MCCARTY@RED.RUTGERS.EDU
Subject: Conference - AI and Law
FINAL CALL FOR PAPERS:
First International Conference on
ARTIFICIAL INTELLIGENCE AND LAW
May 27-29, 1987
Northeastern University
Boston, Massachusetts, USA
In recent years there has been an increased interest in the applications of
artificial intelligence to law. Some of this interest is due to the potential
practical applications: A number of researchers are developing legal expert
systems, intended as an aid to lawyers and judges; other researchers are
developing conceptual legal retrieval systems, intended as a complement to the
existing full-text legal retrieval systems. But the problems in this field are
very difficult. The natural language of the law is exceedingly complex, and it
is grounded in the fundamental patterns of human common sense reasoning. Thus,
many researchers have also adopted the law as an ideal problem domain in which
to tackle some of the basic theoretical issues in AI: the representation of
common sense concepts; the process of reasoning with concrete examples; the
construction and use of analogies; etc. There is reason to believe that a
thorough interdisciplinary approach to these problems will have significance
for both fields, with both practical and theoretical benefits.
The purpose of this First International Conference on Artificial Intelligence
and Law is to stimulate further collaboration between AI researchers and
lawyers, and to provide a forum for the latest research results in the field.
The conference is sponsored by the Center for Law and Computer Science at
Northeastern University. The General Chair is: Carole D. Hafner, College of
Computer Science, Northeastern University, 360 Huntington Avenue, Boston MA
02115, USA; (617) 437-5116 or (617) 437-2462; hafner.northeastern@csnet-relay.
Authors are invited to contribute papers on the following topics:
- Legal Expert Systems
- Conceptual Legal Retrieval Systems
- Automatic Processing of Natural Legal Texts
- Computational Models of Legal Reasoning
In addition, papers on the relevant theoretical issues in AI are also invited,
if the relationship to the law can be clearly demonstrated. It is important
that authors identify the original contributions presented in their papers, and
that they include a comparison with previous work. Each submission will be
reviewed by at least three members of the Program Committee (listed below), and
judged as to its originality, quality and significance.
Authors should submit six (6) copies of an Extended Abstract (6 to 8 pages) by
January 15, 1987, to the Program Chair: L. Thorne McCarty, Department of
Computer Science, Rutgers University, New Brunswick NJ 08903, USA; (201)
932-2657; mccarty@rutgers.arpa. Notification of acceptance or rejection will
be sent out by March 1, 1987. Final camera-ready copy of the complete paper
(up to 15 pages) will be due by April 15, 1987.
Conference Chair: Carole D. Hafner Northeastern University
Program Chair: L. Thorne McCarty Rutgers University
Program Committee: Donald H. Berman Northeastern University
Michael G. Dyer UCLA
Edwina L. Rissland University of Massachusetts
Marek J. Sergot Imperial College, London
Donald A. Waterman The RAND Corporation
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End of AIList Digest
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