Explanation-based acquisition of planning operators

Classical planning algorithms require that their operators be simple in order for planning to be tractable. However, the complexities of real world domains suggest that, in order to be accurate, planning operators must be complex. We demonstrate how, by taking advantage of background knowledge and the distribution of planning problems encountered, it is possible to automatically construct planning operators that are both reliable and succinct. The acquired operator is an encapsulated control loop that is specialized to best fit observed world behavior. Succinctness is achieved by publishing to the planner only those conditions required to succeed over the estimated distribution of problems. We demonstrate the acquisition of a context-appropriate "take-off" operator that can successfully control a complex flight simulator.