# Probabilistic programs for inferring the goals of autonomous agents

arXiv: https://arxiv.org/abs/1704.04977 · HTML: https://mlsanity.com/paper/1704.04977/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2017.

## Built on by (most cited first, 4 of 4)

- [From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought](https://mlsanity.com/paper/2306.12672/) (2023)
- [Inferring the Goals of Communicating Agents from Actions and Instructions](https://mlsanity.com/paper/2306.16207/) (2023)
- [Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind](https://mlsanity.com/paper/2208.02914/) (2022)
- [Meta-Learning an Inference Algorithm for Probabilistic Programs](https://mlsanity.com/paper/2103.00737/) (2021)

## Abstract (arXiv)

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.