Modeling Human Plan Recognition using Bayesian Theory of Mind

The human brain is the most powerful plan recognition system we know. Central to the brain’s remarkable plan recognition capacity is a theory of mind (ToM): our intuitive conception of other agents’ mental states – chiefly, beliefs and desires – and how they cause behavior. We present a Bayesian framework for ToM-based plan recognition, expressing generative models of beliefand desire-dependent planning in terms of partially observable Markov decision processes (POMDPs), and reconstructing an agent’s joint belief state and reward function using Bayesian inference, conditioned on observations of the agent’s behavior in the context of its environment. We show that the framework predicts human judgments with surprising accuracy, and substantially better than alternative accounts. We propose that “reverse engineering” human ToM by quantitatively evaluating the performance of computational cognitive models against data from human behavioral experiments provides a promising avenue for building plan recognition systems.