Unifying Classical Planning Approaches

State space and plan space planning approaches have traditionally been seen as fundamentally different and competing approaches to domain-independent planning. We present a plan representation and a generalized algorithm template, called UCP, for unifying these classical planning approaches within a single framework. UCP models planning as a process of refining a partial plan. The alternative approaches to planning are cast as complementary refinement strategies operating on the same partial plan representation. UCP is capable of arbitrarily and opportunistically interleaving plan-space and state-space refinements within a single planning episode, which allows it to reap the benefits of both. We discuss the coverage, completeness and systematicity of UCP. We also present some preliminary empirical results that demonstrate the utility of combining state-space and plan-space approaches. Next, we use the UCP framework to answer the question “which refinement planner is best suited for solving a given population of problems efficiently?” Our approach involves using subgoal interaction analysis. We provide a generalized account of subgoal interactions in terms of plan candidate sets, and use it to develop a set of guidelines for choosing among the instantiations of UCP. We also include some preliminary empirical validation of our guidelines. In a separate appendix, we also describe how the HTN planning approach can be integrated into the UCP framework. This research is supported in part by NSF research initiation award (RIA) IRI-9210997, NSF young investigator award (NYI) IRI-9457634 and ARPA/Rome Laboratory planning initiative grants F30602-93-C-0039 and F30602-95-C-0247. We thank Laurie Ihrig and Amol Dattatreya Mali for helpful comments.