Path Finding under Uncertainty through Probabilistic Inference

TL;DR

This paper presents a novel probabilistic inference framework for path-finding under uncertainty, enabling efficient policy learning and high-performance solutions for complex stochastic problems.

Contribution

It introduces a domain-independent probabilistic modeling approach that separates problem representation from inference, facilitating effective path-finding under uncertainty.

Findings

High-performance stochastic policies for the Canadian Traveler Problem

Effective separation of problem representation and inference

Demonstrated efficiency of probabilistic inference in path-finding

Abstract

We introduce a new approach to solving path-finding problems under uncertainty by representing them as probabilistic models and applying domain-independent inference algorithms to the models. This approach separates problem representation from the inference algorithm and provides a framework for efficient learning of path-finding policies. We evaluate the new approach on the Canadian Traveler Problem, which we formulate as a probabilistic model, and show how probabilistic inference allows high performance stochastic policies to be obtained for this problem.