# Nested Reasoning About Autonomous Agents Using Probabilistic Programs

arXiv: https://arxiv.org/abs/1812.01569 · HTML: https://mlsanity.com/paper/1812.01569/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [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)
- [Neural Amortized Inference for Nested Multi-agent Reasoning](https://mlsanity.com/paper/2308.11071/) (2023)
- [Modeling Others' Minds as Code](https://mlsanity.com/paper/2510.01272/) (2025)
- [Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs](https://mlsanity.com/paper/2108.03834/) (2021)
- [Differentiable User Models](https://mlsanity.com/paper/2211.16277/) (2022)
- [Combining a Meta-Policy and Monte-Carlo Planning for Scalable Type-Based Reasoning in Partially Observable Environments](https://mlsanity.com/paper/2306.06067/) (2023)

## Abstract (arXiv)

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.