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Explanation-based learning for intelligent process planning

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4 Author(s)
S. C. Park ; Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA ; M. T. Gervasio ; M. J. Shaw ; G. F. DeJong

The possibility of applying explanation-based learning (EBL), a technique from machine learning, to intelligent process planning is explored. There are currently two major approaches to process planning: the variant approach and the generative approach. Each has advantages and deficiencies. The authors' hypothesis is that EBL could successfully unite these apparently disparate approaches. EBL can be used to transform 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. It would seem to be a natural vehicle to unite variant and generative process planning. A learning process planner, called EXBLIPP is implemented to test the authors' hypothesis. It is found that the system possesses many of the intended advantages. It is demonstrated that the EBL capability enables the process planning system to learn new schemata which yield many of the advantages of variant process planning

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:23 ,  Issue: 6 )