An adaptive classifier system tree for extending genetics-based machine learning in a dynamic environment

Abstract

An autonomous agent should possess the ability to adapt its cognition structure to a dynamically changing environment. This ability may be achieved when autonomous agents interact with the environment. In this paper, an adaptive classifier system tree is proposed for extending genetics-based machine learning in a dynamic environment. The architecture has the properties of self-similarity and self-organization. When environmental changes are inspected, the autonomous agent can adapt its cognition structure to the new environment so that cognition can be achieved with great efficiency. After a description of the dynamic structure and the principle of the structure’s self-organization, some experiments illustrating how the architecture works are described and discussed.