Probabilistic programs for inferring the goals of autonomous agents
This work addresses a specific challenge in AI for interpreting agent behavior, but it is incremental as it builds on existing probabilistic and path planning methods with limited scope.
The paper tackles the problem of inferring the goals of autonomous agents from partial motion observations by introducing probabilistic programs based on randomized path planning algorithms, and it demonstrates efficacy on three simple examples with under 50 lines of code each.
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.