| Title: |
Towards Adaptive Heuristic Planning through Machine Learning |
| Author(s): |
D. Vrakas, G. Tsoumakas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file.
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| Keywords: |
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| Appeared in: |
Proc. 21st Workshop of the UK Planning and Scheduling Special Interest Group (PlanSIG '02), 2002.
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| 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. |
| See also : |
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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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