  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.
