Nested Reasoning About Autonomous Agents Using Probabilistic Programs

TL;DR

This paper introduces a probabilistic programming framework for nested reasoning in autonomous agents, enabling them to infer other agents' plans in complex pursuit-evasion scenarios with high uncertainty.

Contribution

It develops a novel planning-as-inference approach using probabilistic programs for nested simulation of agent behavior in uncertain environments.

Findings

Demonstrates rational behaviors in pursuit-evasion games

Quantifies the impact of nesting levels on estimator variance

Models complex primitives like field-of-view and path planning

Abstract

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.