Peter Haddawy and Meliani Suwandi
We present a method for abstracting probabilistic condition actions and we show how to compute expected utility bounds for plans containing such abstract actions. We present a planning algorithm that searches through the space of possible plans by building abstract plans, comparing them, and refining only those that might be refinable to the optimal plan. The algorithm is guaranteed to find the optimal plan and with an appropriate abstraction hierarchy has a complexity which is exponentially better than that of exhaustive enumeration. The planning algorithm has been implemented as the DRIPS decision-theoretic refinement planning system.