ICML-98 Submission #101

Genetic Programming and Deductive-Inductive Learning:  A Multistrategy Approach

	* Ricardo Aler, Daniel Borrajo, and Pedro Isasi
		Departamento de Informática, 
                Universidad Carlos III de Madrid,
		28911 Leganés (Madrid), Spain, 
		email: aler@inf.uc3m.es,{dborrajo,isasi}@ia.uc3m.es

Abstract:

  Genetic Programming (GP) is a machine learning technique that was not
  conceived to use domain knowledge for generating new candidate solutions. It
  has been shown that GP can benefit from domain knowledge obtained by other
  machine learning methods with more powerful heuristics. However, it is not
  obvious that a combination of GP and a knowledge intensive machine learning
  method can work better than the knowledge intensive method alone. In this
  paper we present a multistrategy approach where an already multistrategy
  approach ({\sc hamlet} combines analytical and inductive learning) and an
  evolutionary technique based on GP (EvoCK) are combined for the task of
  learning control rules for problem solving in planning. Results show that
  both methods complement each other, supplying to the other method what the
  other method lacks and obtaining better results than using each method
  alone.

Keywords: Genetic Programming, Learning in Planning, Multistrategy learning

Email address of contact author: aler@inf.uc3m.es

Phone number of contact author: +(34-1) 624 9418