This report details our approach to combining dynamic, distributed constraint reasoning with machine learning techniques and adaptive response strategies. By combining these technologies, we built a system that can 1) develop robust, adaptable mission plans 2) exploit knowledge learned through prior interactions with our adversary, and 3) autonomously and dynamically alter its behavior during mission execution to improve the likelihood of a successful outcome. This system has been thoroughly tested in the ATE2 and ATE3 simulators that were provided by AFRL/RI against four increasingly difficult milestones.
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