Generalizing the Role of Determinization in Probabilistic Planning
  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.
