Learning Decision Strategies with Genetic Algorithms

Machine learning offers the possibility of designing intelligent systems that refine and improve their initial knowledge through their own experience. This article focuses on the problem of learning sequential decision rules for multi-agent environments. We describe the SAMUEL learning system that uses genetic algorithms and other competition based techniques to learn decision strategies for autonomous agents. One of the main themes in this research is that the learning system should be able to take advantage of existing knowledge where available. This article describes some of the mechanisms for expressing existing knowledge in SAMUEL, and explores some of the issues in selecting constraints for the learning system.