Proceedings of the Annual Meeting of the Cognitive Science Society

Recent advances in Artificial Intelligence have produced agents that can beat human world champions at games like Go,Starcraft, and Dota2. However, most of these models do not seem to play in a human-like manner: People infer others’intentions from their behaviour, and use these inferences in scheming and strategizing. Here, using a Bayesian Theory ofMind (ToM) approach, we investigated how much an explicit representation of others’ intentions improves performancein a cooperative game. We compared the performance of humans playing with optimal-planning agents with and withoutToM, in a cooperative game where players have to flexibly cooperate to achieve joint goals. We find that teams with ToMagents significantly outperform non-ToM agents when collaborating with all types of partners: non-ToM, ToM, as well ashuman players, and that the benefit of ToM increases the more ToM agents there are. These findings have implications fordesigning better cooperative agents.