Nested Reasoning About Autonomous Agents Using Probabilistic Programs
  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.
