Approximation spaces for hierarchical intelligent behavioral system models

Abstract

This article introduces a hierarchical behavioral model for an intelligent system that is capable of approximate reasoning. The rough set approach introduced by Zdzisław Pawlak provides a ground for concluding to what degree a particular model for an intelligent system is a part of a set of models representing a standard. Each layer of the hierarchical view of an intelligent system includes one or more information systems as well as one or more approximation spaces that provide a framework for approximate reasoning, learning, and pattern recognition. An approach to the solution of the behavioral system model classification problem in the context of rough sets and a satisfaction-based approximation space is suggested in this article. Approximation spaces are used to classify and measure intelligent system behavior patterns. In this context, rough inclusion of information granules relative to a standard and the proximity to a satisfaction threshold are measured. In addition, a rough set approach to ethology in classifying the behavior of cooperating agents is introduced. A hierarchical model for a swarmbot is briefly considered by way of illustration of the approach to modeling and classifying the behavior of intelligent systems.© Springer-Verlag Berlin Heidelberg 2005.