Path Finding under Uncertainty through Probabilistic Inference

David Tolpin; Brooks Paige; Jan Willem van de Meent; Frank Wood

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arXiv · 1502.07314

Path Finding under Uncertainty through Probabilistic Inference

David Tolpin·Brooks Paige·Jan Willem van de Meent·Frank Wood

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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.

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David Tolpin, Brooks Paige, Jan Willem van de Meent, Frank Wood. 2015-02-25. Path Finding under Uncertainty through Probabilistic Inference. https://arxiv.org/abs/1502.07314

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