Nathaniel G. Martin and James F. Allen
We discuss three difficulties in applying decision theory directly to planning: the computational complexity of reasoning about probability, the difficulty of gathering knowledge of probability and the difficulty of specifying behavior using utilities. We then describe a decision theoretic planning assistant that avoids some of the problems. In particular, instead of generating plans it assists a traditional planner, thereby avoiding the complexity of generating and evaluating plans. It reasons about probability based on its experience, so does not require precise probabilities supplied in advance and its behavior is specified through goals.