Representations for action selection learning from real-time observation of task experts

Mark A. Wood, Joanna J. Bryson

Research output: Contribution to journalConference article

Abstract

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.

Original languageEnglish
Pages (from-to)641-646
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
Publication statusPublished - 1 Dec 2007
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: 6 Jan 200712 Jan 2007

ASJC Scopus subject areas

  • Artificial Intelligence

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