Generalizing the Role of Determinization in Probabilistic Planning
This work addresses the computational hardness in probabilistic planning for AI researchers, offering incremental improvements over existing determinization methods.
The authors tackled the problem of improving determinization-based planners for stochastic shortest path problems by learning tailored determinizations and incorporating probabilistic reasoning, resulting in the FF-LAO* planner that outperformed state-of-the-art methods on competition benchmarks.
The stochastic shortest path problem (SSP) is a highly expressive model for probabilistic planning. The computational hardness of SSPs has sparked interest in determinization-based planners that can quickly solve large problems. However, existing methods employ a simplistic approach to determinization. In particular, they ignore the possibility of tailoring the determinization to the specific characteristics of the target domain. In this work we examine this question, by showing that learning a good determinization for a planning domain can be done efficiently and can improve performance. Moreover, we show how to directly incorporate probabilistic reasoning into the planning problem when a good determinization is not sufficient by itself. Based on these insights, we introduce a planner, FF-LAO*, that outperforms state-of-the-art probabilistic planners on several well-known competition benchmarks.