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)
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)
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)
Domain independent AI planning systems must be able to efficiently generate solutions if they are to be widely deployed to solve large scale real world problems. This paper suggests a static domain analysis technique that can be used to derive heuristics (called general memos) that can be used by Graphplan, one of the most efficient algorithms for solving(More)