Third International Conference on Multistrategy Learning
Ryszard S. Michalski, General Chair;
Janusz Wnek, Program Chair
May 23–25, 1996, Harpers Ferry, West Virginia. Published by The AAAI Press, Menlo Park, California. This proceedings is available in book format.
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Contents
MSL-96 Organization/ viii
Preface / x
I. Theoretical Issues
Multistrategy Learning: When, How and Why / 3
Lorenza Saitta
Using Background Knowledge to Build Multistrategy Learners / 10
Claude Sammut
A Multistrategy Approach to Relational Knowledge Discovery in Databases
/ 16
Katharina Morik and Peter Brockhausen
Induction in Logic / 27
Luc De Raedt
Induction as Knowledge Integration / 37
Benjamin D. Smith and Paul S. Rosenbloom
Fusing the Results of Diverse Algorithms / 50
John F. Elder IV
Learning Synthesis Schemes in Intelligent Systems / 54
Lech Polkowski and Andrzej Skowron
II. Cognitive Models
The Basic Level and Privilege in Relation to Goals, Theories, and Similarity /
68
Douglas L. Medin, Elizabeth B. Lynch, John D. Coley, and Scott Atran
Coevolution Learning: Synergistic Evolution of Learning Agents
and
Problem Representations / 81
Lawrence Hunter
A Comparison of Action Selection Learning Methods / 90
Diana Gordon and Devika Subramanian
A Cognitive Modeling Approach to Learning of Ill-defined Categories /
98
Mukesh Rohatgi
III. Methods and Systems
Multistrategy Task-adaptive Learning Using Dynamically Interlaced Hierarchies:
A Methodology and Initial Implementation of INTERLACE / 112
Nabil W. Alkharouf and Ryszard S. Michalski
Combining Symbolic and Numeric Methods for Learning to Predict Temporal
Series / 121
Marco Botta and Attilio Giordana
An Empirical Study of Computational Introspection: Evaluating
Introspective Multistrategy Learning in the Meta-AQUA System / 131
Michael T. Cox
From Instances to Rules: A Comparison of Biases / 143
Pedro Domingos
Multistrategy Learning to Apply Cases for Case-Based Reasoning / 151
David B. Leake, Andrew Kinley and David Wilson
Inductive Logic Programming + Stochastic Bias = Polynomial Approximate
Learning / 161
Michèle Sebag, Céline Rouveirol and Jean-Francois Puget
Theory Restructuring: Coarse-grained Integration of Strategies for
Induction and Maintenance of Knowledge Bases / 172
Edgar Sommer
Decision Combination Based on the Characterization of Predictive
Accuracy / 186
Kai Ming Ting
A Multistrategy Learning System for Planning Operator Acquisition /
198
Xuemei Wang
On-line Metalearning in Changing Contexts: METAL(B) and METAL(IB) /
211
Gerhard Widmer
Learning Weighted Prototypes Using Genetic Algorithms / 223
Jianping Zhang and Qiu Fan
IV. Special Topics and Applications
Revising User Profiles: The Search for Interesting Web Sites / 232
Daniel Billsus and Michael Pazzani
CAM-BRAIN: ATR’s Billion Neuron Artificial Brain Project / 244
Hugo de Garis
Integrating EBL and ILP to Acquire Control Rules for Planning / 263
Tara A. Estlin and Raymond J. Mooney
Forecasting of Options in the Car Industry Using a Multistrategy
Approach / 272
Stefan Ohl
How to Predict It: Inductive Prediction by Analogy Using Taxonomic
Information / 285
Takashi Ishikawa and Takao Terano
Addressing Knowledge Discovery Problems in a Multistrategy Framework /
294
Kenneth Kaufman
Automated Extraction of Expert System Rules from Databases Based on
Rough Set Theory / 302
Shusaku Tsumoto and Hiroshi Tanaka
Comparative Analysis of Amino-acid Sequences Based on Rough Set Theory
and Change of Representation / 314
Shusaku Tsumoto and Hiroshi Tanaka
Application of Multistrategy Learning in Finance / 322
Martin Westphal and Gholamreza Nakhaeizadeh
Index / 327