MINDS AND MACHINES
http://www.wkap.nl/journalhome.htm/0924-6495
SPECIAL ISSUE: MACHINE LEARNING AS EXPERIMENTAL PHILOSOPHY OF SCIENCE
GUEST EDITORS: KEVIN KORB AND HILAN BENSUSAN
Deadline: 1 December 2003
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Machine learning studies inductive strategies in algorithms. The
philosophy of science investigates inductive strategies as they appear
in scientific practice. Although the two disciplines have developed
largely independently, they share many of the same underlying
problems. This is slowly coming to be recognized in a number of ways,
as evidenced in the annual Uncertainty in AI and AI and Statistics
conferences. This special issue will explore the extent to which the
methods and resources of philosophy of science and machine learning
can inform one another.
In "Computational Philosophy of Science" (1988) Paul Thagard presented
a challenge to the philosophical community: philosophical theories of
scientific method, if they are worth their salt, should be
implementable as computer programs. Contributions will address this
challenge and/or the inverse challenge: both machine learning
algorithms and methods for evaluating machine learning algorithms
should be implementations of sensible approaches to philosophy of
science. Machine learning researchers have only recently discovered
the relevance of statistics and philosophical views on the foundations
of statistics to evaluating the performance of their systems; we hope
to carry that discussion further.
The special issue will therefore focus on such questions as:
1. Can machine learning experiments tell us about inductive discovery
in science?
2. What theoretical results in computational learning can be useful in
understanding scientific method? How can accounts of scientific
confirmation, explanation, discovery and consilience be used to
develop automatic learning systems?
3. How can we assess induction? What statistical or other criteria
need to be met to prefer one machine learning algorithm and/or
scientific method over another? What is the role in machine learning
and science of model building versus prediction?
4. Is there a substantial difference between scientific reasoning as
conceived in the philosophy of science and in machine learning?
5. Is scientific method indeed mechanizable? Are scientific practices
algorithmic?
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Instructions for Authors are available at
http://www.kluweronline.com/issn/0924-6495
Inquiries and papers should be sent to:
[EMAIL PROTECTED]
or
Dr Kevin Korb
School of Computer Science
and Software Engineering
Monash University
Clayton, Victoria 3800
Australia
