Learning rules for adaptive planning
Dimitris Vrakas, Grigorios Tsoumakas, Nick Bassiliades, Ioannis P. Vlahavas · 2003
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 sys-tem, whose knowledge has been acquired through machine learning techniques. Specifically, the algorithm of classifi-cation 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 com-petitions. The paper presents experimental results with the adaptive planner, which demonstrate the boost in perform-ance of the planning system.