Strategy learning for reasoning agents

Authors:
Hendrik Skubch;Michael Thielscher
Affiliations:
Department of Computer Science, Technische Universität Dresden, Germany;Department of Computer Science, Technische Universität Dresden, Germany
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

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Abstract

We present a method for knowledge-based agents to learn strategies. Using techniques of inductive logic programming, strategies are learned in two steps: A given example set is first generalized into an overly general theory, which then gets refined. We show how a learning agent can exploit background knowledge of its actions and environment in order to restrict the hypothesis space, which enables the learning of complex logic program clauses. This is a first step toward the long term goal of adaptive, reasoning agents capable of changing their behavior when appropriate.