Representations for Action Selection Learning from Real-Time Observation of Task Experts

Mark A. Wood, Joanna J. Bryson

The association of perception and action is key to learning by observation in general, and to program-level task imitation in particular. The question is how to structure this information such that learning is tractable for resource-bounded agents. By introducing a combination of symbolic representation with Bayesian reasoning, we demonstrate both theoretical and empirical improvements to a general-purpose imitation system originally based on a model of infant social learning. We also show how prior task knowledge and selective attention can be rigorously incorporated via loss matrices and Automatic Relevance Determination respectively.

Subjects: 12. Machine Learning and Discovery; 1.8 Game Playing

Submitted: Oct 16, 2006

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