A computational model for the cognitive immune system theory based on learning classifier systems

Authors:
Daniel Voigt;Henry Wirth;Werner Dilger
Affiliations:
Chemnitz University of Technology, Chemnitz, Germany;Chemnitz University of Technology, Chemnitz, Germany;Chemnitz University of Technology, Chemnitz, Germany
Venue:
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Year:
2007

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Abstract

In the past there have been several approaches to use Learning Classifier Systems (LCS) as a tool for modelling the functioning of the immune system. In this paper we propose a modification of the classic LCS that can be used for modelling the Cognitive Immune System Theory introduced by I. Cohen. It has been pointed out before that this alternative view of the immune system and its agents provides promising functional perspectives to the field of artificial immune systems (AIS). The characteristic features of Cohen's theory, namely degeneracy of recognition and context of immune reactions, and how they can be realized in our modified LCS are described. Moreover, we introduce the representations of the immune agents, the interactions that take place among them and the applied evolutionary mechanisms.