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Lecture Notes in Computer Science
We describe a relational learning by observation framework that automatically creates cognitive agent programs that model expert task performance in complex dynamic domains. Our framework uses observed behavior and goal annotations of an expert as the primary input, interprets them in the context of background knowledge, and returns an agent program that behaves similar to the expert. We map the problem of creating an agent program on to multiple learning problems that can be represented in adoi:10.1007/978-3-540-30109-7_17 fatcat:ctgsu5tbanchboj74qsxs74aky