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Publication Details

  Author(s): D. Vrakas, G. Tsoumakas, N. Bassiliades, I. Vlahavas.

Title: “Learning Rules for Adaptive Planning”.

Availability: Click here to download the PDF (Acrobat Reader) file (10 pages).

Keywords: planning and learning, domain-independent classical planning, machine learning, knowledge based systems.

Appeared in: Proc. 13th International Conference on Automated Planning and Scheduling (ICAPS '03), pp. 82-91, Trento, Italy, June 2003, 2003.

Abstract: This paper presents a novel idea, which combines Planning, Machine Learning and Knowledge-Based techniques. It is concerned with the development of an adaptive planning system that can fine-tune its planning parameters based on the values of specific measurable characteristics of the given planning problem. Adaptation is guided by a rule-based system, whose knowledge has been acquired through machine learning techniques. Specifically, the algorithm of classification based on association rules was applied to a large dataset produced by results from experiments on a large number of problems used in the three AIPS Planning competitions. The paper presents experimental results with the adaptive planner, which demonstrate the boost in performance of the planning system.

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