Although skeletal plan refinement is used in several planning systems, a procedure for the automatic acquisition of such high-level plans has not yet been developed. The proposed explanation- based knowledge acquisition procedure constructs a skeletal plan automatically from a sophisticated concrete planning case. The classification of that case into a well-described class of problems serves as an instrument for adjusting the applicability of the acquired skeletal plans to that class. The four phases of the proposed procedure are constituted as follows: In the first phase, the execution of the source plan is simulated, and explanations for the effects of the occurred operators are constructed. In the second phase, the generalization of these explanations is performed with respect to a criterion of operationality which specifies the vocabulary for defining abstract operators for the skeletal plan. The third phase, a dependency analysis of the resulting operator effects, unveils the interactions of the concrete plan which are substantial for the specified class. In the forth phase, the concept descriptions for the abstract operators of the skeletal plan are formed by collecting and normalizing the important constraints for each operation that were indicated by the dependencies. With this procedure sophisticated planning solutions from human experts can be generalized into skeletal plans and consequently be reused by a planning system in novel situations.
Abstraction is one of the most promising approaches to improve the performance of problem solvers. Abstraction by dropping sentences of a domain description - as used in most hierarchical planners - is known to be very representation dependent. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract.
Recently, the use of abstraction in case-based reasoning (CBR) is getting more and more popular. The basic idea is to supply a CBR system with cases at many different levels of abstraction. When a new problem must be solved, one (or several) 'appropriate' concrete or abstract case are retrieved from the case base and the solution that the case contains is reused to derive a solution for the current problem, e.g. by filling in the details that a retrieved case at some higher level of abstraction does not contain. A major problem that occurs when using this approach is, that for a given new problem, usually several cases, e.g., from different levels of abstraction could be reused to solve the new problem. Choosing a wrong abstract case can slow down the problem solving process or even prevents the problem from being solved.
Hierachical planning can be improved by explanation-based learning (EBL) of abstract plans from detailed, successfully solved planning problems. Abstract plans, expressed in well-established terms of the domain, serve as useful problem decompositions which can drastically reduce the planning complexity. The learned plan abstraction must be valid for a class of planning cases rather than for a single case, to ensure their successful application in a larger spectrum of new situations. A hierarchical organization of the newly learned knowledge must be archieved to overcome the utility problem in EBL. This paper presents a new formal model of shared plan abstraction and the closely related explanation-based procedure S-PABS. Unlike other apporaches to plan abstraction, our model allows a total different terminology to be introduced at the abstract level. Finally, an unsupervised incremental procedure for constructing a hierachy of shared abstract plans is proposed, as a kind of concept formation over explanations.