Planning, the generation of a course of action to achieve a set of goals, is an important technique in the development of intelligent agents. Heretofore, planning has been largely considered as a one-shot problem. However, in practice, we are often dealing with situations in which an existing plan has to be adapted. Not only might we be facing a dynamic environment that requires a plan to be repaired, but it may also be that we recognize the new planning problem as being similar to one that we have solved before (i.e. case-based planning). This paper investigates a plan adaptation framework based on SAT-encodings of the planning problem. Compilation techniques have been very successfully applied to planning, as evidenced by their success in recent planning competitions. So far, however, such techniques have not been used for plan adaptation purposes. This paper explores whether it is feasible to modify the generated SAT instances such as to encode information that was extracted from the solution to the original planning problem.
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