Representations for Learning Action Selection from Real-Time Observation of Task Experts
Wood, M. A. and Bryson, J. J., 2007. Representations for Learning Action Selection from Real-Time Observation of Task Experts. In: 20th International Joint Conference on Artificial Intelligence, 2007-01-06 - 2007-01-12.
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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.
|Item Type||Conference or Workshop Items (Paper)|
|Creators||Wood, M. A.and Bryson, J. J.|
|Departments||Faculty of Science > Computer Science|
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