Learning and rewriting in fuzzy rule graphs

Abstract

Different learning algorithms based on learning from examples are described based on a set of graph rewrite rules. Starting from either a very general or a very special rule set which is modeled as graph, two to three basic rewrite rules are applied until a rule graph explaining all examples is reached. The rewrite rules can also be used to model the corresponding hypothesis space as they describe partial relations between different rule set graphs. The possible paths, algorithms can take through the hypothesis space can be described as application sequences. This schema is applied to general learning algorithms as well as to fuzzy rule learning algorithms.