Call for papers

ECML'2001 Workshop:

Machine Learning as Experimental Philosophy of Science

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 issues. This is slowly coming to be recognized, as evidenced in the annual Uncertainty in AI and AI and Statistics conferences. This workshop 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. In this workshop we will address this challenge and also the inverse challenge to machine learning researchers: 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 this workshop will carry that discussion further.

The workshop will therefore focus on such questions as:


  1. How machine learning experiments and results can inform our knowledge of scientific inductive discovery?
  2. What theoretical results in computational learning can be useful to understand scientific methods?

    3. How accounts of confirmation, explanation, discovery and theoretical unification developed in the philosophy of science area can be used to develop automatic learning systems?
  3. How induction is to be assessed: is empirical adequacy (predictive accuracy) enough both to account for scientific dynamics and to evaluate automated induction performance?
  4. Is there a substantial difference between scientific reasoning as conceived in the philosophy of science and in artificial intelligence?
  5. Is scientific method mechanisable? Are scientific practices algorithmic?

Venue

This workshop is one of a number of workshops jointly sponsored by the 12th European Conference on Machine Learning (ECML'01) 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'01). Have a look at their workshop program.

Invited Speakers

Publication

Accepted papers will be published in the first instance as workshop notes and on the web. Authors are invited to revise their articles in the light of the discussions at the workshop and submit them to a special issue we have arranged with the Journal for Experimental and Theoretical Artificial Intelligence.

Important Dates

Papers due: 8 June 2001
Notification: 25 June 2001
Camera-ready due: 13 July 2001
Workshop: 3 Sept 2001

Submission Instructions

We prefer papers to be submitted electronically in a postscript email attachment to both organizers simultaneously (i.e., to hilanb@unb.br and korb@csse.monash.edu.au). Only if strictly necessary, submissions may be sent alternatively as an MS Word attachment. A last resort would be to mail or fax submissions to the address below.
MLEPS Workshop
c/o Kevin B. Korb
School of Computer Science
Monash University
Clayton, VIC 3800
AUSTRALIA

Fax: +61 (03) 9905-5146

Workshop Organisers

 Kevin Korb (Monash University, Australia)

Hilan Bensusan (Bristol University, UK)

Program Committee