Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
A Learning Classifier Systems Bibliography
Learning Classifier Systems, From Foundations to Applications
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Hi-index | 0.00 |
In this paper, recent research results are presented which demonstrate the effectiveness of a rule learning system in two dynamic system control tasks. This system, called a learning classifier system (LCS), learns rules to control a simple Internal object and a simulated natural gas pipeline. Starting from a randomly generated state of mind, the learning classifier system learns string-rules called classifiers which match strings called messages. Messages are sent by environmental sensors or by previously activated classifiers. Each classifier's effectiveness is evaluated by an internal service economy complete with bidding and action. Furthermore, new rules are created by an innovative search mechanism called a genetic algorithm. Genetic algorithms are search algorithms based on the mechanicsm of natural genetics. Results from computational experiments in both tasks are presented. In the internal object task, the LCS learns an effective set of rules to center the object repeatedly. In the pipeline task, the LCS learns to control the pipeline under normal summer and winter conditions. It also learns to alarm correctly for the presence or absence of a leak. These results demonstrate the effectiveness of the learning classifier system approach and suggest further refinements which are currently under Investigation.