Muhammad Afzal Upal

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Domain independent planners can produce better-quality plans through the use of domain-speciic knowledge, typically encoded as search control rules. The planning-by-rewriting approach has been proposed as an alternative technique for improving plan quality. We present a system that automatically learns plan rewriting rules and compare it with a system that(More)
Considerable planning and learning research h a s b e e n devoted to the problem of automatically acquiring search control knowledge to improve planning eciency. However, most speed up learning systems deene planning success rather narrowly, namely as the production of any plan that satisses the goals regardless of the quality of the plan. As planning(More)
Considerable work has been done to automatically learn domain-specific knowledge to improve the performance of domain independent problem solving systems. However, most of this work has focussed on learning search control knowledge-knowledge that can be used by a problem solving system during search to improve its performance. An alternative approach to(More)
Plan rationale has been variously deened as \why t h e plan is the way it is", and as \the reason as to why the planning decisions were taken" (PT98). The usefulness of storing plan rationale to help future planning has been demonstrated by several types of case-based planners. However, the existing techniques are unable to distinguish between planning(More)
Unsupervised classiication is the classiication of data into a number of classes in such a way that data in each class are all similar to each other. In the past there have been few if any studies done to compare the performance of diierent unsupervised classiication techniques. In this paper we review Bayesian and neural net approaches to unsupervised(More)