PSA Newsletter: Vol. 1 No. 3: September 1995
PSA Newsletter: Vol. 1 No. 3: September 1995
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- EDITOR'S NOTE
- Next PSA Meeting and Call for Papers
- Matchette Lectures in Philosophy, Science, and Mathematics
- Special Journal Issue on Scientific Discovery
Subject: 1. EDITOR'S NOTE:
Directions for subscribing and unsubscribing:
SUBSCRIBE PSA-NEWS <your name> To unsubscribe, include the following as the ONLY line:
Subject 2. Next PSA Meeting and Call for Papers
The Fifteenth Biennial Meeting of the Philosophy of Science Association will be held November 1-3, 1996, at the Stouffer Renaissance Cleveland Hotel at Tower City Center in Cleveland, Ohio.
CALL FOR PAPERS PHILOSOPHY OF SCIENCE ASSOCIATION FIFTEENTH BIENNIAL MEETING
Contributed papers may be on any topic in the philosophy of science. Maximum length is 5000 words, counting footnotes and references. If the text includes tables or figures, an appropriate number of words should be subtracted from the limit. Two copies, each including a 100 word abstract and a word count should be submitted in double-spaced typescript. Format and citation style should match those of Philosophy of Science. (See a recent issue for details.) If the paper is too long or the references incomplete, the paper will be returned to the author. Papers will be blind refereed; therefore, the author's name, institutional affiliation, surface and email addresses, and fax and telephone numbers should appear on a separate page. Hard copy of submissions must reach the chair of the program committee by November 15, 1995. Accepted papers will be published prior to the meeting in a supplemental issue of the journal Philosophy of Science. Notification about the status of submissions will be mailed in late January or early February. A finished manuscript (one hard copy and one on floppy disk, the latter in IBM or Macintosh format, using a standard word processor) must be submitted by March 1, 1996. Authors of accepted papers are expected to present abbreviated versions of their papers, with a time limit of 20 minutes (plus discussion).
Address program inquiries and paper submissions to:
- Lindley Darden, Chair 1996 PSA Program Committee
- Department of Philosophy
- 1125A Skinner Building
- University of Maryland
- College Park, MD 20742 USA
- 301-405-5699 (office)
- 301-474-0037 (home)
- 301-405-5690 (fax)
The Program Committee consists of: Lindley Darden, Chair (University of Maryland, College Park), Ron Amundson (University of Hawaii-Hilo), John Earman (University of Pittsburgh), Daniel Hausman (University of Wisconsin), Tim Maudlin (Rutgers University), W. H. Newton-Smith (Oxford University), Rose-Mary Sargent (Merrimack College), Paul Thagard (University of Waterloo).
3. Matchette Lectures in Philosophy, Science, and Mathematics
4. Special Journal Issue on Scientific Discovery
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: Nov 1, 1995 Appearance: Scheduled for early 1996
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.
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