Abstract
Conventional Artificial Intelligence (AI) techniques have been criticized for their brittleness under dynamically changing environments. In recent years, therefore, much attention has been focused on the reactive planning approach such as behavior-based AI. However, in the behavior-based artificial AI approach, there are following problems that have to be resolved: 1) how do we construct an appropriate arbitration mechanism, and 2) how do we prepare appropriate behavior primitives (competence modules). On the other hand, biological information processing systems have various interesting characteristics viewed from the engineering standpoint. Among them, in this study, we particularly pay close attention to the immune system. We try to construct a decentralized consensus-making mechanism inspired by the immune network hypothesis. To tackle the above-mentioned problems in the behavior-based AI, we apply the proposed method to behavior arbitration for an autonomous mobile robot by carrying out some simulations and experiments using a real robot. In addition, we investigate two types of adaptation mechanisms to construct an appropriate artificial immune network without human intervention.
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© 1998 Springer-Verlag Berlin Heidelberg
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Ishiguro, A., Watanabe, Y., Kondo, T., Uchikawa, Y. (1998). Artificial Immune Network and Its Application to Robotics. In: Jain, L.C., Fukuda, T. (eds) Soft Computing for Intelligent Robotic Systems. Studies in Fuzziness and Soft Computing, vol 21. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1882-6_2
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DOI: https://doi.org/10.1007/978-3-7908-1882-6_2
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-13003-2
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