Learning Rules for Adaptive Planning

D. Vrakas, G. Tsoumakas, N. Bassiliades, I. Vlahavas, “Learning Rules for Adaptive Planning”, Proc. 13th International Conference on Automated Planning and Scheduling (ICAPS '03), pp. 82-91, Trento, Italy, June 2003, 2003.

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

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

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.