The Minds of Many: Opponent Modeling in a Stochastic Game.

The Theory of Mind provides a framework for an agent to predict the actions of adversaries by building an abstract model of their strategies using recursive nested beliefs. In this paper, we extend a recently introduced technique for opponent modelling based on Theory of Mind reasoning. Our extended multi-agent Theory of Mind model explicitly considers multiple opponents simultaneously. We introduce a stereotyping mechanism, which segments the agent population into sub-groups of agents with similar behaviour. Sub-group profiles guide decision making. We evaluate our model using a multi-player stochastic game, which presents agents with the challenge of unknown adversaries in a partially-observable environment. Simulation results demonstrate that the model performs well under uncertainty and that stereotyping allows larger groups of agents to be modelled robustly. The findings show that Theory of Mind modelling is useful in many artificial intelligence applications.