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Case-Based Learning in Goal-Driven Autonomy Agents for Real-Time Strategy Combat Tasks

We describe a study on using case-based learning techniques in a goal-driven autonomy (GDA) agent for real-time strategy games. The two case bases in our Learning GDA (LGDA) agent store (1) the expected states that an agent can reach when executing an action in and (2) the next goals to pursue when a discrepancy occurs between the expected and encountered states. We report on an ablation study that demonstrates performance gains using LGDA.