Path Finding under Uncertainty through Probabilistic Inference

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

Research output: Working paper/PreprintPreprint


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.
Original languageEnglish
StatePublished - 2015

Publication series

NamearXiv preprint arXiv:1502.07314


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