Copy Link
Add to Bookmark
Report
IRList Digest Volume 4 Number 41
IRList Digest Sunday, 24 July 1988 Volume 4 : Issue 41
Today's Topics:
Announcement - Chemical Information conf., email addresses?
Call for Papers - 2nd Conference on AI & Law
COGSCI - Connectionist Representations and Linguistic Inference
- Michael Lesk on "Does Technology Affect How People Read?"
- Model-Based Diagnostic Reasoning using Past Experiences
News addresses are
Internet or CSNET: fox@vtopus.cs.vt.edu or fox@fox.cs.vt.edu
BITNET: foxea@vtvax3.bitnet (soon will be foxea@vtcc1)
----------------------------------------------------------------------
Date: Thu, 30 Jun 88 12:24:41 EDT
From: David Johnson <DKJOHNS%ERENJ.BITNET@CUNYVM.CUNY.EDU>
Subject: Chemical Information conference, mail addresses?
Ed,
...
The Sheffield address would be appreciated when you have the time, no rush.
[Note: an earlier request was for email address of Peter Willett or
any others at Sheffield; below is a request for address of P.
Ingwersen. - Ed.]
Would you have an ID for Peter Ingwersen?
We have set our chemical information conference for the 3rd week in June
1990. We had to set a date and went on the assumption that SIGIR was
usually about the 2nd week in June. We had other meetings that we wanted
to not conflict with as well and there seemed less overlap with SIGIR (seems
like just Peter Willett and me). We will be meeting at the Leeuwenhorst
Conference Center in Noordwijkerhout, The Netherlands, so a Belgian venue
for SIGIR could be very advantageous.
...
Thanks for your help,
David
------------------------------
Date: Fri, 15 Jul 88 13:27:51 EDT
From: carole hafner <hafner%corwin.ccs.northeastern.edu@RELAY.CS.NET>
Subject: Call for Papers - 2nd Conf. on AI and Law
CALL FOR PAPERS
Second International Conference on
ARTIFICIAL INTELLIGENCE and LAW
June 13-16, 1989
University of British Columbia
Vancouver, British Columbia, Canada
The field of AI and Law -- which seeks both to understand fundamental mechanisms
of legal reasoning as well as to develop useful applications of AI to law --
is burgeoning with accomplishments in both basic research and practical
applications. This increased activity is due in part to more widely available
AI technology, advances in fundamental techniques in AI and increased interest
in the law as an ideal domain for studying certain issues central to AI.
The activities range from development of classic expert systems, intended as
aids to lawyers and judges, to investigation of canonical elements of case-based
and analogical reasoning. The study of AI and law both draws on and contributes
to progress in basic concerns in AI, such as representation of common sense
knowledge, example-based learning, explanation, and non-monotonic reasoning,
and in jurisprudence, such as the nature of legal rules and the doctrine
of precedent.
The Second International Conference on Artificial Intelligence and
Law (ICAIL-89) seeks to stimulate further collaboration between workers in
both disciplines, provide a forum for sharing information at the cutting
edge of research and applications, spur further research on fundamental
problems in both the law and AI, and provide a continuing focus for the
emerging AI and law community.
Authors are invited to contribute papers on topics such as the following:
-- Legal Expert Systems
-- Conceptual Information Retrieval
-- Case-Based Reasoning
-- Analogical Reasoning
-- Representation of Legal Knowledge
-- Computational Models of Legal Reasoning
In addition, papers on relevant theoretical issues in AI (e.g., concept
acquisition, mixed paradigm systems using rules and cases) and in
jurisprudence/legal philosophy (e.g., open-textured predicates, reasoning
with precedents and rules) are also invited provided that the relationship
to both AI and Law is clearly demonstrated. It is important that all authors
identify the original contributions presented in their papers, exhibit
understanding of relevant past work, discuss the limitations as well as
the promise of their ideas, and demonstrate that the ideas have matured
beyond the proposal stage. Each submission will be reviewed by at least three
members of the Program Committee and judged as to its originality, quality,
and significance.
Authors should submit six (6) copies of an Extended Abstract, which must include
a full list of references, by January 10, 1989 to the Program Chair:
Edwina L. Rissland
Department of Computer and Information Science
University of Massachusetts, Amherst, MA 01003, USA;
(413) 545-0332, rissland@cs.umass.edu.
Submissions should be 6 to 8 pages in length, not including references.
No electronic submissions can be accepted. Notification of acceptance or
rejection will be sent out by early March. Final camera-ready copy of the
complete paper (up to 15 pages) will be due by April 15, 1989.
Program Chair: Edwina L. Rissland, University of Massachusetts/Amherst and
Harvard Law School
General Co-Chairs: Robert T. Franson, Joseph C. Smith, Faculty of Law,
University of British Columbia
Secretary-Treasurer: Carole D. Hafner, Northeastern University
Program Kevin D. Ashley IBM Thomas J. Watson Reasearch Center
Committee: Trevor J.M. Bench-Capon University of Liverpool
Donald H. Berman Northeastern University
Jon Bing University of Oslo
Michael G. Dyer UCLA
Anne v.d.L. Gardner Palo Alto, California
L. Thorne McCarty Rutgers University
Marek J. Sergot Imperial College London
------------------------------
Date: Fri, 6 May 1988 12:55 EDT
From: Peter de Jong <DEJONG%OZ.AI.MIT.EDU@XX.LCS.MIT.EDU>
Subject: Cognitive Science Calendar [Extract - Ed.]
Date: Friday, 29 April 1988 13:48-EDT
From: reiter at harvard.harvard.edu (Ehud Reiter)
Re: Harvard AI seminar
Monday, May 9, 1988
4 PM
Aiken 101 (Harvard University)
(Tea at 3:45 pm, Aiken Main Lobby)
Connectionist Representations and Linguistic Inference
David S. Touretzky
Computer Science Department
Carnegie Mellon University
DUCS is a neural network architecture for representing and manipulating
frame-like structures. Slot names and slot fillers are diffuse patterns of
activity spread over a collection of units. The choice of a distributed
representation gives rise to certain useful properties not shared by
conventional frame systems. One of these is the ability to retrieve a slot
even if the slot name is not known precisely. Another is the ability to encode
fine semantic distinctions as subtle variations on the canonical pattern for a
slot. DUCS combines the flexiblity of parallel distributed processing with the
structured flavor of conventional formalisms. but it is only suggestive of the
sort of fluid knowledge representations connectionists are really after.
In the second half of the talk I will discuss some current problems in
connectionist natural language processing. Spreading activation/lateral
inhibition architectures are insufficient to handle many interesting linguistic
phenomena. For example, metonymy requires not only a rich knowledge
representation, but also a flexible inference mechanism. Future connectionist
models, employing more sophisticated network architectures, may provide
solutions to these difficulties.
------------------------------
Date: Thu, 9 Jun 88 11:19:33 EDT
From: Peter de Jong <dejong@WHEATIES.AI.MIT.EDU>
Subject: Cognitive Science Calendar [Extract - Ed.]
Date: Tue, 7 Jun 88 17:04:44 edt
From: Laureen Fletcher <fletch%eyes@media-lab.media.mit.edu>
Subject: Talk by Michael Lesk
"Does Technology Affect How People Read?"
Lessons from the 18th Century. This is about reprinting the first
edition of "Tristram Shandy;" duplicating 18th century fonts, etc.
with some discussion of the switch from reading aloud to reading
silently.
"How to Tell a Pine Cone
from an Ice Cream Cone -- Sense
Disambiguation Using Machine
Readable Dictionaries"
Does a "fireman" feed fires or put them out? It depends on whether or
not he is on a steam locomotive. This talk explains a scheme for
deciding which sense of an ambiguous word is meant by counting
overlaps of words in definitions in a machine-readable dictionary.
Michael Lesk
Division Manager
of Computer Sciences Research
Bell Communications Research
Morristown, New Jersey
Friday, June 10, 1988
2:00 - 3:00 p.m.
E15-401
Host: Peg Schafer
------------------------------
Date: Fri, 10 Jun 88 16:53:11 EDT
From: Peter de Jong <dejong@WHEATIES.AI.MIT.EDU>
Subject: Cognitive Science Calendar
Date: Fri 10 Jun 88 13:52:39-EDT
From: Marc Vilain <MVILAIN@g.bbn.com>
Subject: BBN AI Seminar -- Phylis Koton
BBN Science Development Program
AI Seminar Series Lecture
MODEL-BASED DIAGNOSTIC REASONING USING PAST EXPERIENCES
Phylis Koton
MIT Lab for Computer Science
(ELAN@XX.LCS.MIT.EDU)
BBN Labs
10 Moulton Street
2nd floor large conference room
10:30 am, Tuesday June 14
The problem-solving performance of most people improves with experience.
The performance of most expert systems does not. People solve
unfamiliar problems slowly, but recognize and quickly solve problems
that are similar to those they have solved before. People also remember
problems that they have solved, thereby improving their performance on
similar problems in the future. This talk will describe a system,
CASEY, that uses case-based reasoning to recall and remember problems it
has seen before, and uses a causal model of its domain to justify
re-using previous solutions and to solve unfamiliar problems.
CASEY overcomes some of the major weaknesses of case-based reasoning
through its use of a causal model of the domain. First, the model
identifies the important features for matching, and this is done
individually for each case. Second, CASEY can prove that a retrieved
solution is applicable to the new case by analyzing its differences from
the new case in the context of the model. CASEY overcomes the speed
limitation of model-based reasoning by remembering a previous similar
case and making small changes to its solution. It overcomes the
inability of associational reasoning to deal with unanticipated problems
by recognizing when it has not seen a similar problem before, and using
model-based reasoning in those circumstances.
The techniques developed for CASEY were implemented in the domain of
medical diagnosis, and resulted in solutions identical to those derived
by a model-based expert system for the same domain, but with an increase
of several orders of magnitude in efficiency. Furthermore, the methods
used by the system are domain-independent and should be applicable in
other domains with models of a similar form.
------------------------------
END OF IRList Digest
********************
