Dynamic System Control Using Rule Learning and Genetic Algorithms

Abstract

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 lnertlal 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 auction. Furthermore, new rules are created by an innovative search mechanism called a genetic algorithm. Genetic algorithms are search algorithms based on the mechanics of natural genetics. Results from computational experiments in both tasks are presented. In the inertial 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. I BACKGROUND Many industrial tasks and machines that once required human intervention have been all but completely automated. Where once a person tooled a part, a machine tools, senses, and tools again. Where once a person controlled a machine, a computer controls, senses, and continues its task. Repetitive tasks requiring a high degree of precision have been most susceptible to these extreme forms of automated control. Yet despite these successes, there are still many tasks and This paper describes work done at the University of Michigan. It was partially supported by U. S.