Abstract. This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliate uses CBR to allow RL to respond more quickly to changing conditions. CBRetaliate combines two key features: it uses a time window to compute similarity and stores and reuses complete Q-tables for continuous problem solving. We demonstrate CBRetaliate on a team-based first-person shooter game, where our combined CBR+RL approach adapts quicker to changing tactics by an opponent than standalone RL.

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We describe our progress on instrumenting a Navy software simulator for use in the context of intelligent agent research. The Tactical Action Officer (TAO) Sandbox, which is being developed at the University of Southern California, is used by officers to practice tactical decision making in the context of Navy surface fleet missions. NRL and Knexus Research Corporation have integrated this simulator with intelligent agents using the Lightweight Integration and Evaluation Testbed (LIET), thus permitting the agent to play the role of a trainee. This will permit us to use the TAO Sandbox in our artificial intelligence research, where we are currently focusing on algorithms for continuous planning that can dynamically reason about what goal should be pursued at any time during a mission. This paper briefly descibes our motivation for this integration, project status involving this simulator, and future goals.

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