Abstract
We propose an adaptive algorithm based on some features of the immune system (a selection-based mechanism compatible with Edelman’s selectionist principle, self/nonself reference, and negative/positive selection). The algorithm proceeds in three steps: diversity generation, establishment of self-tolerance, and memorizing nonself. This algorithm may typically be used to model the system of distributed agents where the system (the self) as well as the environment (the nonself) are unknown or cannot be modeled. An agent-based architecture based on the local memory hypothesis and a network-based architecture based on the network hypothesis are discussed. The agent-based architecture is elaborated with applications to an adaptive system where knowledge about the environment is not available. An adaptive noise neutralizer is formalized and simulated for a simple plant.
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Some part of this work has been presented at ICEC 1996, IROS 1996, and AROB 1999.
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Ishida, Y. An adaptive algorithm based on the immune system and its application to adaptive noise neutralization. Artif Life Robotics 5, 171–177 (2001). https://doi.org/10.1007/BF02481465
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DOI: https://doi.org/10.1007/BF02481465