  Intelligent systems sometimes need to infer the probable goals of people,
cars, and robots, based on partial observations of their motion. This paper
introduces a class of probabilistic programs for formulating and solving these
problems. The formulation uses randomized path planning algorithms as the basis
for probabilistic models of the process by which autonomous agents plan to
achieve their goals. Because these path planning algorithms do not have
tractable likelihood functions, new inference algorithms are needed. This paper
proposes two Monte Carlo techniques for these "likelihood-free" models, one of
which can use likelihood estimates from neural networks to accelerate
inference. The paper demonstrates efficacy on three simple examples, each using
under 50 lines of probabilistic code.
