AIList-REQUEST@SRI-AI.ARPA (AIList Moderator Kenneth Laws) (10/21/85)
AIList Digest Sunday, 20 Oct 1985 Volume 3 : Issue 150
Today's Topics:
Seminars - Program Logics (UPenn) &
Meaning, Information and Possibility (UCB) &
Machine Learning and Knowledge Representation (NU) &
Intelligent Mail Manipulation (MIT) &
A Logic for Defeasible Rules (Buffalo) &
Learning From Multiple Analogies (GTE) &
Computational Discourse Analysis Using DEREDEC (MIT) &
RESEARCHER and Patent Analogies (CMU)
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Date: Mon, 14 Oct 85 19:26 EDT
From: Tim Finin <Tim%upenn.csnet@CSNET-RELAY.ARPA>
Subject: Seminar - Program Logics (UPenn)
REASONING ABOUT PROGRAMS: CONCEPTUAL AND METHOLOGICAL DISTINCTIONS
DANIEL LEIVANT, COMPUTER SCIENCE, CMU
3:00 pm Tuesday, October 15, 1985
216 Moore, University of Pennsylvania
Reasoning about programs can be done explicitly, in first-order or higher-order
mathematical theories, or implicitly, in modal logics of programs (Hoare Logic,
Dynamic Logic...). One wants the latter, but the former are better suited for
metamathematical analysis (semantics, calibration of proof-theoretic strength).
However, modal logics are interpretable in explicit theories, so we can eat the
cake and have it.
In particular, we can distinguish in modal logics of programs a purely logical
component and an analytical component. For example, Hoare's Logic captures
exactly logical reasoning about partial-correctness assertions over
WHILE-programs. We argue that this type of completeness is more informative
than relative completeness.
------------------------------
Date: Wed, 16 Oct 85 14:22:50 PDT
From: admin@ucbcogsci.Berkeley.EDU (Cognitive Science Program)
Subject: Seminar - Meaning, Information and Possibility (UCB)
BERKELEY COGNITIVE SCIENCE PROGRAM
Cognitive Science Seminar - IDS 237A
Tuesday, October 22, 11:00 - 12:30
240 Bechtel Engineering Center
Discussion: 12:30 - 1:30 in 200 Building T-4
``Meaning, Information and Possibility''
L. A. Zadeh
Computer Science Division, U.C. Berkeley
Our approach to the connection between meaning and information
is in the spirit of the Carnap--Bar-Hillel theory of state
descriptions. However, our point of departure is the assump-
tion that any proposition, p, may be expressed as a generalized
assignment statement of the form X isr C, where X is a variable
which is usually implicit in p, C is an elastic constraint on
the values which X can take in a universe of discourse U, and
the suffix r in the copula isr is a variable whose values
define the role of C in relation to X. The principal roles are
those in which r is d, in which case C is a disjunctive con-
straint; and r is c, p and g, in which cases C is conjunctive,
probabilistic and granular, respectively. In the case of a
disjunctive constraint, isd is written for short as is, and C
plays the role of a graded possibility distribution which asso-
ciates with each point (or, equivalently, state-description)
the degree to which it can be assigned as a value to X. This
possibility distribution, then, is interpreted as the informa-
tion conveyed by p. Based on this interpretation, we can con-
struct a set of rules of inference which allow the possibility
distribution of a conclusion to be deduced from the possibility
distributions of the premises. In general, the process of
inference reduces to the solution of a nonlinear program. The
connection between the solution of a nonlinear program and the
traditional methods of deduction in first-order logic are
explained and illustrated by examples.
ELSEWHERE ON CAMPUS
William Clancy of Stanford University will speak on ``Heuristic
Classification'' at the SESAME Colloquium on Monday, Oct. 21,
4:00pm, 2515 Tolman Hall.
Ruth Maki of North Dakota State University will speak on ``Meta-
comprehension: Knowing that you understand'' at the Cognitive
Psychology Colloquium, Friday, October 25, 4:00pm, Beach Room,
3105 Tolman Hall.
------------------------------
Date: Thu, 17 Oct 85 15:02 EDT
From: Carole D Hafner <HAFNER%northeastern.csnet@CSNET-RELAY.ARPA>
Subject: Seminar - Machine Learning and Knowledge Representation (NU)
Northeastern University
College of Computer Science Colloquium
4p.m. Wednesday, October 30
Brittleness, Tunnel Vision, Machine Learning and
Knowledge Representation
Prof. Steve Gallant
Northeastern University
A system is brittle if it fails when presented with slight deviations from
expected input. This is a major problem with knowledge representation schemes
and particularly with expert systems which use them.
This talk defines the notion of Tunnel Vision and shows it to be a major
cause of brittleness. As a consequence it will be claimed that commonly
used schemes for machine learning and knowledge representation are pre-
disposed toward brittle behavior. These include decision trees, frames,
and disjunctive normal form expressions.
Some systems which are free from tunnel vision will be described.
Place: 405 Robinson Hall
Northeastern University
360 Huntington Ave.
Boston MA
------------------------------
Date: Sun, 13 Oct 1985 16:53 EDT
From: Peter de Jong <DEJONG%MIT-OZ at MIT-MC.ARPA>
Reply-to: Cog-Sci-Request%MIT-OZ
Subject: Seminar - Intelligent Mail Manipulation (MIT)
[Forwarded from the MIT bboard by SASW@MIT-MC.]
Thursday 17, October 4:00pm Room: NE43- 8th floor Playroom
The Artificial Intelligence Lab
Revolving Seminar Series
"The Information Lens:
An Intelligent System for Finding, Filtering, and
Sorting Electronic Messages"
Thomas W. Malone
MIT Sloan School of Management
This talk will describe an intelligent system to help people share,
filter, and sort information communicated by computer-based messaging
systems. The system exploits concepts from artificial intelligence such
as frames, production rules, and inheritance networks, but it avoids the
unsolved problems of natural language understanding by providing users
with a rich set of semi-structured message templates. A consistent set
of "direct manipulation" editors simplifies the use of the system by
individuals, and an incremental enhancement path simplifies the adoption
of the system by groups.
The talk will also include an overview of the other projects and
research goals in the Organizational Systems Laboratory at MIT.
------------------------------
Date: Fri, 18 Oct 85 08:30:03 EDT
From: "William J. Rapaport" <rapaport%buffalo.csnet@CSNET-RELAY.ARPA>
Subject: Seminar - A Logic for Defeasible Rules (Buffalo)
UNIVERSITY AT BUFFALO
STATE UNIVERSITY OF NEW YORK
DEPARTMENT OF
COMPUTER SCIENCE
COLLOQUIUM
DONALD NUTE
Advanced Computational Methods Center
and Department of Philosophy
University of Georgia
A LOGIC FOR DEFEASIBLE RULES
Humans reason using defeasible and sometimes conflicting rules
like `Matches burn when struck' and `Wet things don't burn'. A
formal language for representing sentential versions of such
rules is presented together with a derivability relation for this
language. The resulting system, LDR, is non-monotonic. Inspired
by work in conditional logic, the non-monotonic rules of LDR
correspond to simple subjunctive and `might' conditionals.
Chaining of these rules is restricted in LDR just as the transi-
tivity of the conditional is restricted in conditional logics.
Several notions of consistency and coherency are defined. LDR is
of special importance for research in automated reasoning, since
its language is PROLOG-like and its derivability relation can be
implemented in PROLOG.
Thursday, November 7, 1985
3:30 P.M.
Bell 337, Amherst Campus
Wine and cheese will be served at 4:30 P.M., 224 Bell Hall
For further information, contact:
William J. Rapaport
Assistant Professor
Dept. of Computer Science, SUNY Buffalo, Buffalo, NY 14260
(716) 636-3193, 3181
uucp: ...{allegra,decvax,watmath}!sunybcs!rapaport
...{cmc12,hao,harpo}!seismo!rochester!rocksvax!sunybcs!rapaport
cs/arpanet: rapaport%buffalo@csnet-relay
------------------------------
Date: Fri, 18 Oct 85 23:44:39 EDT
From: Bernard Silver <SILVER@MIT-MC.ARPA>
Subject: Seminar - Learning From Multiple Analogies (GTE)
GTE LABS INCORPORATED
MACHINE LEARNING SEMINAR
Title: Learning from Multiple Analogies
Speaker: Mark H. Burstein
BBN Labs.
Date: Monday October 21, 10am
Place: GTE Labs
40 Sylvan Rd, Waltham MA 02254
Students learning about an unfamiliar new subject under the guidance
of a teacher or textbook, are often taught basic concepts by analogies
to things that they are more familiar with. Although this seems to
be a very powerful form of instruction, the process by which students
make use of this kind of instruction has been little studied by AI
learning theorists. A cognitive process model of how students make
use of such analogies will be presented. The model was motivated by
examples of the behavior of several students who were tutored on the
programming language BASIC, and focusses in detail on the development
of knowledge about the concept of a program variable, and its use in
assignment statements. It suggests how several analogies can be used
together to form new concepts where no one analogy would have been
sufficient. Errors produced by one reasoning from one analogy can
be corrected by another.
As an illustration of the main principles of the model, a computer
program, CARL, is presented that learns to use variables in BASIC
assignment statements. While learning about variables, CARL generates
many of the same erroneous hypotheses seen in the recorded protocols
of students learning the same material given the same set of analogies.
The learning process results in a single target model that retains
some aspects of each of the analogies presented.
For more information, contact Bernard Silver (617) 576-6212
------------------------------
Date: 11am 10/22/85
From: Alker@mc
Subject: Seminar - Computational Discourse Analysis Using DEREDEC (MIT)
[Forwarded from the MIT bboard by SASW@MIT-MC.]
Computational Discourse Analysis Using DEREDEC:
An Analysis of Balzac's Sarrasine
Jaqueline Leon and Jean-Marie Marandin
Centre National de la Recherche Scientifique
Paris, France
We present research in computational discourse analysis and discuss an
example for the case of Balzac's Sarrasine. We use P. Plante's
DEREDEC programming system in this work because of its suitability for
natural language processing. After a bottom-up syntactic parser for
French grammar produces a syntactic derivation, we perform pattern
matching on the output to achieve a linguistic and literary
interpretation. We describe how we use these programs to capture two
different aspects of a text: the thematic segmentation and density.
Time: 11-12:30, Tuesday, October 22, 1985
Place: Millikan Room, E53-482
Host: Professor Hayward R. Alker, Jr., Department of Political Science, MIT
------------------------------
Date: 18 Oct 85 10:14:51 EDT
From: Jeanne.Bennardo@CMU-RI-ISL1
Subject: Seminar - RESEARCHER and Patent Analogies (CMU)
Topic: Presentation of RESEARCHER project.
Speaker: John C. Akbari
Place: DH3313
Date: Wednesday, Oct. 23
Time: 10:00am - 11:00am
Speaker:
John C. Akbari is a Masters student at Columbia University's Department of
Computer Science. He is interested in joining the Intelligent Systems
Laboratory's Phoenix project. Below is a description of his artificial
intelligence research.
Both projects described below investigate different aspects of RESEARCHER, a
prototype intelligent information system being developed at Columbia
University under the direction of Professor Michael Lebowitz. The domain of
investigation is disc drive patents. The result of this research is being
implemented in LISP as a component of RESEARCHER.
MS Thesis
Research involves generating "catalogue descriptions" of
hierarchical objects, determining salience as a function of
similarity between an instance of an object and the
prototype of the object. This will be used in generating
information to be passed to a case grammar generator to
produce the actual text. We hope to develop a method of
determining importance of static information (via "filtering
through" the prototype) relative to context. We are studying
the interaction of structural, attributive, and functional
information on the quality of the description. Further work
will investigate the need for different prototypes for
different users as an aspect of user modelling, so that a
patent lawyer would receive a different description from an
engineer, given the same instance.
Thesis advisor: Prof. Michael Lebowitz
Natural language
We are enhancing RESEARCHER's parser to utilize syntactic
aspects of relations that cause focus of attention to shift
within sentences. This involves modifying memory-based
parsing to determine when syntax cues are sufficiently
strong to over-ride the need to search memory.
Supervisor: Prof. Michael Lebowitz
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End of AIList Digest
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