Emergent behavior in classifier systems

Abstract The paper presents examples of emergent behavior in classifier systems, focusing on symbolic reasoning and learning. These behaviors are related to global dynamical properties such as state cycles, basins of attraction, and phase transitions. A mapping is defined between classifier systems and an equivalent dynamical system (Boolean networks). The mapping provides a way to understand and predict emergent classifier system behaviors by observing the dynamical behavior of the Boolean networks. The paper reports initial results and discusses the implications of this approach for classifier systems.

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