Learning Rules for Adaptive Planning

Dimitris Vrakas, Grigorios Tsoumakas, Nick Bassiliades, and Ioannis Vlahavas

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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