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An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game

An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game

Malgorzata Lucinska, Slawomir T. Wierzchon
Copyright: © 2012 |Pages: 23
ISBN13: 9781609608187|ISBN10: 1609608186|EISBN13: 9781609608194
DOI: 10.4018/978-1-60960-818-7.ch503
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MLA

Lucinska, Malgorzata, and Slawomir T. Wierzchon. "An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game." Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 1192-1214. https://doi.org/10.4018/978-1-60960-818-7.ch503

APA

Lucinska, M. & Wierzchon, S. T. (2012). An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game. In I. Management Association (Ed.), Machine Learning: Concepts, Methodologies, Tools and Applications (pp. 1192-1214). IGI Global. https://doi.org/10.4018/978-1-60960-818-7.ch503

Chicago

Lucinska, Malgorzata, and Slawomir T. Wierzchon. "An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game." In Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, 1192-1214. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-818-7.ch503

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Abstract

Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they will require the ability to cooperate, coordinate, and negotiate with each other. From a theoretical point of view such systems require a hybrid approach involving game theory, artificial intelligence, and distributed programming. On the other hand, biology offers a number of inspirations showing how these interactions are effectively realized in real world situations. Swarm organizations, like ant colonies or bird flocks, provide a spectrum of metaphors offering interesting models of collective problem solving. Immune system, involving complex relationships among antigens and antibodies, is another example of a multi-agent and swarm system. In this chapter an application of so-called clonal selection algorithm, inspired by the real mechanism of immune response, is proposed to solve the problem of learning strategies in the pursuit-evasion problem.

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