Learning Classifier Systems

1. Learning Classifier Systems Dominic Cockman, Jesper Madsen, Qiuzhen Zhu

2. Learning ClassifierSystems History and Motivations

3. History and Motivations for LCS • Robust machine learning techniques that can be applied to classification tasks, large-scale data mining problems or robot control and cognitive system applications, among others • Origins in John Holland’s work on cognitive systems, based on his research into adaption in natural and artificial systems • LCS was introduced as a cognitive systems framework, based on psychology principles and ideas from Darwinian evolution

4. History and Motivations for LCS • A computer program is said to learn from experience with respect to some class of tasks and performance measure, if its performance at tasks in this class, as measured by our chosen performance measure, improves with experience • The required components of a successful LCS are: • A set of classifiers • Some evolution mechanism, either for the classifier or the population, designed to improve rule structures over time • Some evolution mechanism, either for the classifier or the population, which identifies the quality of the rule or population of rules

5. History and Motivations for LCS • LCS follows a ‘mixture of experts’ approach • The object of a learning system, natural or artificial, is the expansion of its knowledge in the face of uncertainty • Ideal learning systems confront some subset of: • A perpetually stream of data from the environment • Continual requirements for action • Implicitly defined goals and sparse payoff - requiring long sequences of action.

6. History and Motivations for LCS • An LCS is "adaptive" - ability to choose the best action improves with experience • Evolution takes place in the background • Effect of classifier evolution is to modify their conditions • Classifier systems are intended as a framework that uses genetic algorithms to study learning in condition/ action, rule-based systems

7. History and Motivations for LCS • Classifier systems address three basic problems in machine learning: • Parallelism and coordination • Credit assignment • Rule discovery • LCS address all these issues

8. History and Motivations for LCS • Recent years have seen much effort put into LCS research and development • Advances have been made in many areas: • Different representations of conditions beyond the traditional binary/ ternary rules • Developments in other problem classes • Smarter exploration mechanisms • Theoretical advancements

9. Learning ClassifierSystems Characteritics & Implementations

10. Minimal Classifier System Source: Fig. 3, Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

11. Michigan and Pittsburg • Two main styles of LCSs • Primary differences: • Population characterization • Learning style

12. LCS Variations • Strength-based (Zeroth-level Classifier System) • Fitness based on the expected reward of the classifier • Accuracy-based (eXtended Classifier System) • Fitness based on the accuracy of the classifier • Discovery (Coverage and GA)

13. Learning ClassifierSystems Applications

14. classification tasks • graph coloring： • it is a way of coloring the vertices of a graph such that no two adjacent vertices share the same color; this is called a vertex coloring，using the least colors. • A crossover derivative was developed toaccommodate the graph coloring specifications. • Also, amethod for verifying and validating the new offspringgenerated via genetic action, conforming to the graphcoloring rules and regulations was considered. • LCS:the condition of the classifier is the graph itself(connections between vertices), and the action componentis a valid coloring assignment for different vertices

15. data mining • EpiCS knowledge discovery in epidemiologic surveillance • LCS：output for:risk estimate,prediction of class membership • input:population situation

16. robot control • mobile robot control • LCS:input:sensory sensing the environment output:desired action

17. MODELLING • prediction on the transaction of the stock market • LCS:input:stock market performance output:people's behaviours

18. other usage • military domain • phsychology modelling • traffic control • chemical reaction control • cognitive system • more...

19. References • Dr. J. Bacardit, Dr. N. Krasnogor. 2013. Introduction to Learning Classifier Systems. [ONLINE] Available at:http://www.exa.unicen.edu.ar/escuelapav/cursos/bio/l7.pdf. [Accessed 22 August 14] • J Bacardit, E Bernado-Mansilla & M V Butz. 2013. Learning Classifier Systems: Looking Back and Glimpsing Ahead. [ONLINE] Available at: http://www.cs.nott.ac.uk/~jqb/publications/LCS-Looking-Glimsing.pdf. [Accessed 23 August 14] • J Holland, L Booker, et al.. [UNKNOWN].What is a Learning Classifier System?. [ONLINE] Available at:http://www.cs.unm.edu/~forrest/publications/LearningClassifierSystems-00.pdf. [Accessed 23 August 14] • L Booker, D Goldberh & J Holland. 1989. Classifier Systems and Genetic Algorithms. [ONLINE] Available at:http://deepblue.lib.umich.edu/bitstream/handle/2027.42/27777/0000171.pdf. [Accessed 23 August 14] • Ezziane, Z, 1998. Learning Classifier System for Graph Coloring. Expert Systems, Volume 15, Issue 4, 240-246. • Holmes, J, Durbin, D & Winston, F, 2000. The Learning Classifier System: An Evolutionary Computation Approach to Knowledge Discovery in Epidemiologic Surveillance. Artificial Intelligence in Medicine, Volume 19, Issue 1, 53-74.

20. References • Hurst, J & Bull, L, 2000. A Self-adaptive Neural Learning Classifier System with Constructivism for Mobile Robot Control. Lecture Notes in Computer Science, Volume 3242, 942-951. • Li, P, Stolzmann, W & Wilson, S, 2000. Learning Classifier Systems: From Foundations to Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence). 2000 Edition. Springer. • Urbanowicz, R & Moore, J, 2009. Learning Classifier Systems: A Complete Introduction, Review, and Roadmap. Journal of Artificial Evolution and Applications, Volume 2009, Article ID 736398.