  As autonomous agents become more ubiquitous, they will eventually have to
reason about the plans of other agents, which is known as theory of mind
reasoning. We develop a planning-as-inference framework in which agents perform
nested simulation to reason about the behavior of other agents in an online
manner. As a concrete application of this framework, we use probabilistic
programs to model a high-uncertainty variant of pursuit-evasion games in which
an agent must make inferences about the other agents' plans to craft
counter-plans. Our probabilistic programs incorporate a variety of complex
primitives such as field-of-view calculations and path planners, which enable
us to model quasi-realistic scenarios in a computationally tractable manner. We
perform extensive experimental evaluations which establish a variety of
rational behaviors and quantify how allocating computation across levels of
nesting affects the variance of our estimators.
