Refining Human Behavior Models in a Context-based Architecture

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This paper describes an investigation into the refinement of context-based human behavior models through the use of experiential learning. Specifically, a tactical agent was endowed with a context-based control model developed through other means and tasked with a mission in a simulation. This simulation-based mission was employed to expose the agent to situations possibly not considered in the model’s original construction. Reinforcement learning was used to evaluate and refine the performance of this agent to improve its effectiveness and generality.