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We introduce a new approach to solving path-finding problems underuncertainty by representing them as probabilistic models and applyingdomain-independent inference algorithms to the models. This approach separatesproblem representation from the inference algorithm and provides a frameworkfor efficient learning of path-finding policies. We evaluate the new approachon the Canadian Traveler Problem, which we formulate as a probabilistic model,and show how probabilistic inference allows high performance stochasticpolicies to be obtained for this problem.

Path Finding under Uncertainty through Probabilistic Inference