CBR Applied to Planning

Abstract

Planning means constructing a course of actions to achieve a specified set of goals, starting from an initial situation. For example, determining a sequence of actions (a plan) for transporting goods from an initial location to some destination is a typical planning problem in the transportation domain. Many planning problems are of practical interest.

The classical generative planning process consists mainly of a search through the space of possible operators to solve a given problem. For most practical problems, this search is intractable. Therefore, case-based reasoning can be a useful idea because it transfers previous solutions rather than searching from scratch.

Since the space of possible plans is typically vast, it is extremely unlikely that a case base contains a plan that can be reused without any modification. Modification has been addressed in CHEF (Hammond 1986), one of the first case-based planners. It retrieves cooking recipes and adapts them to the new problem by using domain specific knowledge. As experience has shown, however, this kind of adaptation in realistic domains requires a large amount of very specific domain knowledge and lacks flexibility.

Given that classical generative planning may involve a very great search effort and pure case-based planning may encounter insurmountable modification needs, several researchers have pursued a synergistic approach of generative and case-based planning. In a nutshell, the case-based planner provides plans previously generated for similar situations and the generative planner is used as a source of modification. In this paper, we present four systems that integrate generative and case-based planning: PRIDIGY/ANALOGY developed at the CMU, CAP^LAN/C^BC and P^ARIS developed at the University of Kaiserslautern, and A^BALONE developed at the Universities of Saarbrücken and Edinburgh. These systems are domain-independent case-based planners that accumulate and use planning cases to control the search. In these systems, cases encode knowledge of which operators were used for solving problems and why. In our synergistic systems, the workload imposed on the generative planner depends on the amount of modification that is required to completely adapt a retrieved case.