Machine Learning for Adaptive Planning
Dimitris Vrakas (Aristotle University of Thessaloniki, Greece), Grigorios Tsoumakas (Aristotle University of Thessaloniki, Greece), Nick Bassiliakes (Aristotle University of Thessaloniki, Greece) and Ioannis Vlahavas (Aristotle University of Thessaloniki, Greece)
Copyright: © 2005
This chapter is concerned with the enhancement of planning systems using techniques from Machine Learning in order to automatically configure their planning parameters according to the morphology of the problem in hand. It presents two different adaptive systems that set the planning parameters of a highly adjustable planner based on measurable characteristics of the problem instance. The planners have acquired their knowledge from a large data set produced by results from experiments on many problems from various domains. The first planner is a rule-based system that employs propositional rule learning to induce knowledge that suggests effective configuration of planning parameters based on the problem’s characteristics. The second planner employs instance-based learning in order to find problems with similar structure and adopt the planner configuration that has proved in the past to be effective on these problems. The validity of the two adaptive systems is assessed through experimental results that demonstrate the boost in performance in problems of both known and unknown domains. Comparative experimental results for the two planning systems are presented along with a discussion of their advantages and disadvantages.