Local Models for data-driven learning of control policies for complex systems
Expert Systems with Applications: An International Journal
Learning collaborative team behavior from observation
Expert Systems with Applications: An International Journal
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This paper describes a two-phase approach for automating the agent-building process when the agent is to perform tactical tasks. The research is inspired by how humans learn-first by observation of a teacher's performance and then by practicing the performance themselves. The objectives of this approach are to produce a high-performing agent that 1) approaches or exceeds the proficiency of a human and 2) does so in a human-like manner. We accomplish these objectives by combining observational learning with experiential learning. These processes are executed sequentially, with the former creating a competent but somewhat limited human-like model from scratch, and the latter improving its performance without significantly eroding its human-like qualities. The process is described in detail, and test results confirming our hypothesis are described.