Control Of Liquid Level Via Learning Classifier System

Abstract

A learning classifier system (LCS) that learns rules for controlling a mathematical model of the liquid level in a vessel has been developed by the Bureau of Mines. LCSs resemble familiar production rule-based systems that incorporate a human expert's knowledge. However, in classifier systems the production rules are represented by strings of characters rather than in linguistic terms. This paper presents two specific examples in which an LCS produces a rule set for controlling liquid level whose performance is comparable to the performance of a human expert's rule set. In the first example, the LCS learns a rule that has been deleted from an author-supplied data base of effective rules. In the second example, the LCS learns rules to supplement a set of rules provided by the authors which included one rule detrimental to controlling liquid level. The LCS-generated rule set obtains a higher level of control over the liquid level system.