Path Finding under Uncertainty through Probabilistic Inference
Artificial Intelligence
2015-06-09 v3
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.
Cite
@article{arxiv.1502.07314,
title = {Path Finding under Uncertainty through Probabilistic Inference},
author = {David Tolpin and Brooks Paige and Jan Willem van de Meent and Frank Wood},
journal= {arXiv preprint arXiv:1502.07314},
year = {2015}
}