No subject

[cfields@NMSU.Edu]

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The following are abstracts of papers appearing in the second issue
of the Journal of Experimental and Theoretical Artificial
Intelligence, to appear in April, 1989.

For submission information, please contact either of the editors:

Eric Dietrich                           Chris Fields
PACSS - Department of Philosophy        Box 30001/3CRL
SUNY Binghamton                         New Mexico State University
Binghamton, NY 13901                    Las Cruces, NM 88003-0001

dietrich at bingvaxu.cc.binghamton.edu     cfields at nmsu.edu

JETAI is published by Taylor & Francis, Ltd., London, New York, Philadelphia

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Generating plausible diagnostic hypotheses with self-processing causal
networks

Jonathan Wald, Martin Farach, Malle Tagamets, and James Reggia

Department of Computer Science, University of Maryland

A recently proposed connectionist methodology for diagnostic problem
solving is critically examined for its ability to construct problem
solutions.  A sizeable causal network (56 manifestation nodes, 26
disorder nodes, 384 causal links) served as the basis of experimental
simulations.  Initial results were discouraging, with less than
two-thirds of simulations leading to stable solution states
(equilibria).  Examination of these simulation results identified a
critical period during simulations, and analysis of the connectionist
model's activation rule during this period led to an understanding of
the model's nonstable oscillatory behavior.  Slower decrease in the
model's control parameters during the critical period resulted in all
simulations reaching a stable equilibrium with plausible solutions.
As a consequence of this work, it is possible to more rationally
determine a schedule for control parameter variation during problem
solving, and the way is now open for real-world experimental
assessment of this problem solving method.

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Organizing and integrating edge segments for texture discrimination

Kenzo Iwama and Anthony Maida

Department of Computer Science, Pennsylvania State University

We propose a psychologically and psychophysically motivated texture
segmentation algorithm.  The algorithm is implemented as a computer
program which parses visual images into regions on the basis of
texture.  The program's output matches human judgements on a very
large class of stimuli.

The program and algorithm offer very detailed hypotheses of how humans
might segment stimuli, and also suggest plausible alternative
explanations to those presented in the literature.  In particular,
contrary to Julesz and Bergen (1983), the program does not use
crossings as textons and does use corners as textons.  Nonetheless,
the program is able to account for the same data.  The program
accounts for much of the linking phenomena of Beck, Pradzny, and
Rosenfeld (1983).  It does so by matching structures between feature
maps on the basis of spatial overlap.  These same mechanisms are also
used to account for the feature integration phenomena of Triesman (1985).

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Towards a paradigm shift in belief representation methodology

John Barnden

Computing Research Laboratory, New Mexico State University

Research programs must often divide issues into managable sub-issues.
The assumption is that an approach developed to cope with a sub-issue
can later be integrated into an approach to the whole issue - possibly
after some tinkering with the sub-approach, but without affecting its
fundamental features.  However, the present paper examines a case
where an AI issue has been divided in a way that is, apparently,
harmless and natural, but is actually fundamentally out of tune with
the realities of the issue.  As a result, some approaches developed
for a certain sub-issue cannot be extended to a total approach without
fundamental modification.  The issue in question is that of modeling
people's beliefs, hopes, intentions, and other ``propositional
attitudes'', and/or interpreting natural language sentences that
report propositional attitudes.  Researchers have, quite
understandably, de-emphasized the problem of dealing in detail with
nested attitudes (e.g. hopes about beliefs, beliefs about intentions
about beliefs), in favor of concentrating on the sub-issue of
nonnested attitudes.  Unfortunately, a wide variety of approaches to
attitudes are prone to a deep but somewhat subtle problem when they
are applied to nested attitudes.  This problem can be very roughly
described as an AI system's unwitting imputation of its own arcane
``theory'' of propositional attitudes to other agents.  The details of
this phenomenon have been published elsewhere by the author: the
present paper merely sketches it, and concentrates instead on the
methodological lessons to be drawn, both for propositional attitude
research and, more tentatively, for AI in general.  The paper also
summarizes an argument (presented more completely elsewhere) for an
approach to attitude representation based in part on metaphors of
mind that are commonly used by people.  This proposed new research
direction should ultimately coax propositional attitude research out
of the logical armchair and into the pyschological laboratory.

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The graph of a boolean function

Frank Harary

Department of Computer Science, New Mexico State University

(Abstract not available)

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