Computational Game Theory AI for Army Tactical COA Generation Scenarios

Computational game theory is the only sound way of planning against an intelligent adversary: it is the only approach that can properly handle imperfect information (partial observability) and the associated opportunities to deceive and be deceived, as well as a nonstationary intelligent adversary that selects its strategy based on what strategy we select for ourselves.Strategy Robot (SR) has developed the only game-theoretic COA Generation and Execution System (CGES). It computes COAs for Blue and Red simultaneously, taking into account that Blue’s optimal COA depends on Red’s COA and that Red’s optimal COA depends on Blue’s COA. So, unlike simulation, single-sided optimization, and single-sided reinforcement learning, it does not need to assume that the user has correctly guessed the adversary's COA (but it can further capitalize on aspects of it being known).Our computational game theory AI has already generated superhuman COAs for armored combat, beyond-visual-range air combat, and no-limit Texas hold’em poker. In this SBIR project we propose to apply it to the Army's desired tactical COA generation scenarios.  The approach in CGES is based on game-theoretically constructing COAs from COA pieces (COAPs). This enables the planning abstraction to be raised so strong COAs can be generated with a reasonable amount of computation.  In Phase I, we will 1) study to understand the relevant Army scenarios and data sources, 2) identify any necessary generalizations to CGES to address them, 3) design a development strategy for those generalizations, 4) conduct a feasibility study, and 5) develop and demonstrate a proof of concept for CoA recommendation. The work will be conducted in Pittsburgh.