The OAKPLAN Case-Based Planner á

Case-based planning can take advantage of former problemsolving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning problems in the future. In this paper we describe an innovative case-based planning system, called OAKPLAN, which is able to efficiently retrieve planning cases from plan libraries with more than ten thousands elements, heuristically choose a suitable candidate (possibly the best one) and adapt it to provide a good quality solution plan similar to the one retrieved from the case base. Overall, we show that OAKPLAN is competitive with state of the art plan generation systems in terms of number of problems solved, CPU time, plan difference values and plan quality when cases similar to the current planning problem are available into the plan library.

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