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Title: Towards Adaptive Heuristic Planning through Machine Learning
Author(s): D. Vrakas, G. Tsoumakas, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file.
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Appeared in: Proc. 21st Workshop of the UK Planning and Scheduling Special Interest Group (PlanSIG '02), 2002.
Abstract: In domain independent heuristic planning there is a number of planning systems with very good performance on some problems and very poor on others. Few attempts have been made in the past to explain this phenomenon. In this paper we use machine learning techniques to discover knowledge hidden in the dynamics of the planning process that would relate specific characteristics of a planning problem with specific properties of a planning system that lead to good or bad performance. By this, we aim at shedding light to some of the dark areas of heuristic planning and develop an adaptive planner that would be able to optimize its configuration according to the problem at hand.
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        This paper has been cited by the following:

1 U. Scholz, Domain Analysis and Domain Knowledge: Generation, Representation, and Implementation, Doctoral Consortium of the 13th International Conference on Automated Planning and Scheduling, pp. 108-111, Trento, Italy 2003.
2 T. L. McCluskey, Knowledge Engineering for Planning Roadmap, PLANET Network of Excellence, 2003
3 P. Haslum, U. Scholz, Domain Knowledge in Planning: Representation and Use, Workshop on PDDL 13th International Conference on Automated Planning and Scheduling, Trento Italy, 2003.


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