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.
|Number of pages||6|
|Publication status||Published - 2007|
|Event||20th International Joint Conference on Artificial Intelligence - Hyderabad, India|
Duration: 6 Jan 2007 → 12 Jan 2007
|Conference||20th International Joint Conference on Artificial Intelligence|
|Period||6/01/07 → 12/01/07|