Strength or Accuracy: Credit Assignment in Learning Classifier Systems: (Distinguished Dissertations)

The Distinguished Dissertations series is published on behalf of the Conference of Professors and Heads of Computing and the British Computer Society, who annually select the best British PhD dissertations in computer science for publication. The dissertations are selected on behalf of the CPHC by a panel of eight academics. Each dissertation chosen makes a noteworthy contribution to the subject and reaches a high standard of exposition, placing all results clearly in the context of computer science as a whole. In this way computer scientists with significantly different interests are able to grasp the essentials - or even find a means of entry - to an unfamiliar research topic. Machine learning promises both to create machine intelligence and to shed light on natural intelligence. A fundamental issue for either endevour is that of credit assignment, which we can pose as follows: how can we credit individual components of a complex adaptive system for their often subtle effects on the world? For example, in a game of chess, how did each move (and the reasoning behind it) contribute to the outcome? This text studies aspects of credit assignment in learning classifier systems, which combine evolutionary algorithms with reinforcement learning methods to address a range of tasks from pattern classification to stochastic control to simulation of learning in animals. Credit assignment in classifier systems is complicated by two features: 1) their components are frequently modified by evolutionary search, and 2) components tend to interact. Classifier systems are re-examined from first principles and the result is, primarily, a formalization of learning in these systems, and a body of theory relating types of classifier systems, learning tasks, and credit assignment pathologies. Most significantly, it is shown that both of the main approaches have difficulties with certain tasks, which the other type does not.

Table of Contents:
Introduction.- Learning Classifier Systems.- How Strength and Accuracy Differ.- What Should a Classifier System Learn?- Prospects for Adaption.- Classifier Systems and Q-Learning.- Conclusion.- Appendices.- Evaluation of Macroclassifiers.- Example XCS Cycle.- Learning from Reinforcement.- Generalisation Problems.- Value Estimation Algorithms.- Generalised Policy Iteration Algorithms.- Evolutionary Algorithms.- The Origins of Sarsa.- Notation.- References.

Review :
From the reviews: "This book is a monograph on learning classifier systems ! . The main objective of the book is to compare strength-based classifier systems with accuracy-based systems. ! The book is equipped with nine appendices. ! The biggest advantage of the book is its readability. The book is well written and is illustrated with many convincing examples." (Jerzy W. Grzymal-Busse, Mathematical Reviews, Issue 2005 k)