As autonomous agents (such as unmanned aerial vehicles, or UAVs) become more ubiquitous, they are being used for increasingly complex tasks. Eventually, they will have to reason about the mental state of other agents, including those agents' beliefs, desires and goals – so-called Theory of Mind – and make decisions based on that reasoning. We describe increasingly complex theory of mind models of a UAV pursuing an intruder, and show that (1) there is a natural Bayesian formulation to reasoning about the uncertainty inherent in our estimate of another agent's mental state, and that (2) probabilistic programming is a natural way to describe models that involve one agent reasoning about another agent, where the target agent uses complex primitives such as path planners and saliency maps to make decisions. We propose a nested self-normalized importance sampling inference algorithm for probabilistic programs, and show that it can be used with planning-as-inference to simultaneously reason about other agents' plans and craft counter plans. We demonstrate that more complex models lead to improved performance, and that nested modeling manifests a wide variety of rational agent behavior.