Subject: AIJ special issue (new deadline)
From: [email-masked] (Raul Valdes-Perez)
Date: 23 Oct 1995 15:01:13 GMT
Newsgroups: comp.ai

Earlier I posted in this forum the Call-for-Papers for an Artificial
Intelligence Journal special issue on Scientific Discovery, for which
the stated deadline was Nov 1.  The official Call-for-Papers appears
in the August issue of AIJ.

However, the publishers have informed me that subscribers will receive
the "August" issue sometime in the next week.  Since receipt of the
official announcement coincides with the submission deadline, we are
therefore extending the deadline to *** DEC 15 ***.

Raul Valdes-Perez

**********************************************************************

		     CALL FOR PAPERS

		 ARTIFICIAL INTELLIGENCE
	 Special Journal Issue on Scientific Discovery

Editors:  Herbert Simon      (Carnegie Mellon)
          Derek Sleeman      (Aberdeen)
          Raul Valdes-Perez  (Carnegie Mellon)

Advisory Editors: Bruce Buchanan (Pittsburgh), Lindley Darden
(Maryland), Gerd Grasshoff (Hamburg), Pat Langley (ISLE & Stanford),
Jan Zytkow (Wichita State)

Submissions:  Dec 15, 1995
Appearance:   Scheduled for early 1997

In recent years, a substantial number of programs have been built and
studied that perform nonroutine tasks in scientific discovery.  Work
on machine discovery is aimed at exploring and enlarging the scope of
computing within science (scientific inference).  Many of us believe
that the potential for computers in this domain is very extensive, but
there are also rational skeptics.  An excellent way to resolve this
issue is to produce programs that perform scientific work competently,
and to characterize these programs in terms of general architectural
features.  A recent AAAI Spring Symposium on scientific discovery
included both types of contributions.

We seek submissions that address these fundamental issues, especially
descriptions of working programs that achieve some form of creativity.
Such programs may, for example, analyze data to discover descriptive
or explanatory laws, conduct laboratory procedures automatically, plan
experiments and experimental strategies, design instruments and
research procedures, discover or revise appropriate problem
representations, search for relevant data, draw inferences and make
predictions from existing theories. That is, they participate in the
numerous activities that are involved in scientific discovery.
(Programs that perform in areas where computers are already main
players are only relevant for this special issue, if they display the
sort of creativity listed above.)

Other appropriate submissions, not based on the description of a new
program, might draw on the accumulated experience already reported in
the literature to make more general statements about the scope of
computing in science, about how best to extend its scope, or how to
enhance existing programs. Such submissions, while more theoretical,
should be based on the properties of working programs, should be
rooted in empirical evidence, and should bear on scientific practice.

All submissions should discuss the basic AI techniques which they have
used in the course of building their Discovery System.  Given the
maturity of the Scientific Discovery field, we would not expect to
publish, in this special issue, papers whose basic approach is
regression analysis or simple curve fitting.

Where all this may lead was foreseen by Allen Newell [quoted by D.G.
Bobrow and P.J. Hayes in "Artificial Intelligence - Where Are We?"
Artif.Intell.; 25(3), 1985]:

 We should, by the way, be prepared for some radical, and perhaps
 surprising, transformations of the disciplinary structure of science
 (technology included) as information processing pervades it. In
 particular, as we become more aware of the detailed information
 processes that go on in doing science, the sciences will find
 themselves increasingly taking a metaposition, in which doing
 science (observing, experimenting, theorizing, testing, archiving, ...)
 will involve understanding these information processes, and
 building systems that do the object-level science. Then the
 boundaries between the enterprise of science as a whole (the
 acquisition and organization of the knowledge of the world) and AI
 (the understanding of how knowledge is acquired and organized)
 will become increasingly fuzzy.

If you intend to make a submission, please send a brief description
well before the deadline to Raul Valdes-Perez (Computer Science
Department; Carnegie Mellon University; Pittsburgh, PA 15213; USA).
Email to [email-masked] is preferred.