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Abstract

Contingent planning is the task of generating a conditional plan given uncertainty about the initial state and action effects, but with the ability to observe some aspects of the current world state. Contingent planning can be transformed into an And-Or search problem in belief space, the space whose elements are sets of possible worlds. In \cite{CFF}, we introduced a method for implicitly representing a belief state using a propositional formula that describes the sequence of actions leading to that state. This representation trades off space for time and was shown to be quite effective for conformant planning within a heuristic forward-search planner based on the \ff\ system. In this paper we apply the same architecture to contingent planning. The changes required to adapt the search space representation are small. More effort is required to adapt the relaxed planning problems whose solution informs the forward search algorithm. We propose the targeted use of an additional relaxation, mapping the relaxed {\em contingent} problem into a relaxed {\em conformant} problem. Experimental results show that the resulting planning system, \contff, is highly competitive with the state-of-the-art contingent planners \pond\ and \mbp.