Algorithmically Identifying Strategies In Multi-Agent Game-Theoretic Environments

Artificial intelligence (AI) has enormous potential for military applications. Fully realizing the conceived benefits of AI requires effective interactions among Soldiers and computational agents in highly uncertain and unconstrained operational environments. Because AI can be complex and unpredictable, computational agents should support their human teammates by adapting their behavior to the human's elected strategy for a given task, facilitating mutually-adaptive behavior within the team. While some situations entail explicit and easy-to-understand human top-down strategies, more often than not, human strategies tend to be implicit, ad hoc, exploratory, and difficult to describe. In order to facilitate mutually-adaptive human-agent team behavior, computational teammates must identify, adapt, and modify their behaviors to support human strategies with little or no a priori experience. This challenge may be achieved by training learning agents with examples of successful group strategies. Therefore, this paper focuses on an algorithmic approach to extract group strategies from multi-agent teaming behaviors in a game-theoretic environment: predator-prey pursuit. Group strategies are illuminated with a new method inspired from Graph Theory. This method treats agents as vertices to generate a timeseries of group dynamics and analytically compares timeseries segments to identify group coordinated behaviors. Ultimately, this approach may lead to the design of agents that can recognize and fall in line with strategies implicitly adopted by human teammates. This work can provide a substantial advance to the field of human-agent teaming by facilitating natural interactions within heterogeneous teams.