An adaptive planner based on learning of planning performance

Case-based planners often face the problem of incurring more computational cost for retrieving and modifying a case for reuse, than what can be saved by reusing the case. We present a case-based planning system that learns the performance of a given planner (called the default planner) in a training phase and exploits this knowledge to retrieve and reuse cases such that planning effort is saved. The system does not involve any modification of the plan being reused. Furthermore, the system uses a very efficient method for matching a new problem with solved cases. The average-case performance of the system has been found to be significantly better than that of the default planner in a test domain. We hypothesize that this approach can be used to improve the performance of other planners as well. The effectiveness of the system hinges mainly on the learning strategy and or I extraction of the relevant features.