Learning Looping Domain-Specific Planners from Example Plans

Planners are powerful tools for problem solving because they provide a complete sequence of actions to achieve a goal from a particular initial state. Classical planning research has addressed this problem in a domain-independent manner— the same algorithm generates a complete plan for any domain specification. This generality comes at a cost; domainindependent planners have difficulty with large-scale planning problems. To deal with this, researchers have resorted to hand writing domain-specific planners to solve them. An interesting alternative is to use example plans to demonstrate how to solve problems in a particular domain and to use that information to automatically learn domain-specific planners that model the observed behavior. In this paper, we present the ITERANT algorithm for identifing repeated structures in observed plans and show how to convert looping plans into domain-specific template planners, or dsPlanners. Looping dsPlanners are able to apply experience acquired from the solutions to small problems to solve arbitrarily large ones. We show that automatically learned dsPlanners are able to solve large-scale problems much more quickly than are state-ofthe-art general-purpose planners and are able to solve problems many orders of magnitude larger than general-purpose planners can solve.