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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Strategy Learning for Reasoning Agents
verfasst von : Hendrik Skubch, Michael Thielscher
Erschienen in: Machine Learning: ECML 2005
Verlag: Springer Berlin Heidelberg
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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.