Behavioral Pattern Identification Through Rough Set Modelling

This paper introduces an approach to behavioral pattern identification as a part of a study of temporal patterns in complex dynamical systems. Rough set theory introduced by Zdzis law Pawlak during the early 1980s provides the foundation for the construction of classifiers relative to what are known as temporal pattern tables. It is quite remarkable that temporal patterns can be treated as features that make it possible to approximate complex concepts. This article introduces what are known as behavior graphs. Temporal concepts approximated by approximate reasoning schemes become nodes in behavioral graphs. In addition, we discuss some rough set tools for perception modeling that are developed for a system for modelling networks of classifiers. Such networks make it possible to recognize behavioral patterns of objects changing over time. They are constructed using an ontology of concepts delivered by experts that engage in approximate reasoning on concepts embedded in such an ontology. This article also includes examples based on data from a vehicular traffic simulator useful in the identification of behavioral patterns by drivers.