Improving Multi-Agent Cooperation using Theory of Mind
  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 of Mind (ToM) approach, we
investigated how much an explicit representation of others' intentions improves
performance in a cooperative game. We compared the performance of humans
playing with optimal-planning agents with and without ToM, in a cooperative
game where players have to flexibly cooperate to achieve joint goals. We find
that teams with ToM agents significantly outperform non-ToM agents when
collaborating with all types of partners: non-ToM, ToM, as well as human
players, and that the benefit of ToM increases the more ToM agents there are.
These findings have implications for designing better cooperative agents.
