Path Finding under Uncertainty through Probabilistic Inference
This addresses path-finding challenges in uncertain environments, but it appears incremental as it adapts existing probabilistic methods to a specific domain.
The paper tackles path-finding under uncertainty by representing problems as probabilistic models and applying domain-independent inference algorithms, showing high performance stochastic policies for the Canadian Traveler Problem.
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.