This paper explores the possibility of applying explanation-Based Learning (EBL), a technique from machine learning, to intelligent process planning. There are currently two major approaches to process planning: the “variant” and the “generative” approaches. Each has advantages and deficiencies. Our hypothesis was that EBL could successfully unite these apparently disparate approaches. Weak method AI planning can be viewed as subscribing to the generative notion of process planning. Skeletal planning, on the other hand, has much in common with the variant approach. EBL can be employed to transition a traditional weak method planner into a strong method skeletal planner by acquiring strong method concepts which are generalized weak-method explanations of observed episodes. Thus, it would seem to be a natural vehicle to unite variant and generative process planning. We implemented a learning process planner, called EXBLIPP, to test our hypothesis. We found that the system possesses many of the intended advantages. In particular, we demonstrate that the EBL capability enables the process planning system to learn new schemata which yield many of the advantages of variant process planning. Unfortunately, the acquired concepts also manifest an inability to respond to unpredictable features of the environment. Such attributes are unavoidable in process planning applications which require nontrivial scheduling decisions. The root of the problem can be traced to the fact that standard EBL forces all nonoperational decisions to be made a priori, thus leading to a brittleness that greatly limits the benefits of the acquired concepts. We were able to overcome this deficiency through the use of a technique called contingent explanation-based learning. This was implemented as an extension to EXBLIPP. By deferring certain planning decisions until execution time, the extended EXBLIPP is able to adapt to the dynamic environment of a manufacturing system. We discuss the strengths and weaknesses of this approach in the context of integrated planning and scheduling. We speculate on how the techniques might be pushed to integrate control decisions as well.
ASJC Scopus subject areas