Improving Multi-Agent Cooperation using Theory of Mind

TL;DR

This paper demonstrates that incorporating a Bayesian Theory of Mind into agents significantly enhances cooperation performance in multi-agent settings, especially when interacting with humans and other ToM agents.

Contribution

It introduces a Bayesian ToM approach into multi-agent cooperation, showing improved performance over non-ToM agents in collaborative tasks.

Findings

ToM agents outperform non-ToM agents with all partner types.

The benefit of ToM increases with more ToM agents in the team.

ToM improves human-agent collaboration in cooperative games.

Abstract

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.