UCLA

Theory of mind (ToM) refers to the ability to understand oneself’s and others’ mental states. The major difficulty of incorporating ToM into multi-agent planning is updating an agent’s beliefs of others’ beliefs over time, which are probability distributions over distributions. In this work, we propose a novel way to model this nested belief update with a higher computational efficiency, simultaneously providing an approach to learn other agents’ models. We model the belief update by a Markov probability transition, which linearly updates beliefs as distributions. This transition kernel characterizes another agent’s belief update and it is learnable, thus we learn other agents’ models by learning their kernels. We demonstrate the effectiveness of our algorithm in a police-thief game, showing that our agent is able to learn other agents’ belief updates and intentionally change their beliefs to achieve its own goal.