Publication Date




Preliminary version published in Proc. of International Conference on Intelligent Technologies, Bangkok, Thailand, December 13-15, 2000, pp. 17-23; final version published in the International Journal of Uncertainty, Fuzziness, and Knowledge Based Systems, 2001, Vol. 9, No. 6, pp. 827-838.


Since Kautz and Selman's 1992 ECAI paper on satisfiability based planning, there has been several work on planning through finding models of a logical theory. Most of these works focus on finding a plan of a given length. If we want to find the shortest plan, then usually, we try plans of length 1, 2, ..., until we find the first length for which such a plan exists. When the planning problem is difficult and the shortest plan is of a reasonable length, this linear search can take a long time; to speed up the process, it has been proposed (by Lifschitz and others) to use binary search instead. Binary search for the value of a certain parameter x is optimal when for each tested value x, we need the same amount of computation time; in planning, the computation time increases with the size of the plan and, as a result, binary search is no longer optimal. We describe an optimal way of combining planning algorithms into a search for the shortest plan - optimal in the sense of worst-case complexity. We also describe an algorithm which is asymptotically optimal in the sense of average complexity.

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