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.
In order to concentrate on the immune and game theoretic techniques, the rest of the chapter will focus on one class of the multi-agent encounters, i.e. pursuit-evasion problems. They are among the most widespread, challenging, and important multi-agent scenarios and represent some of the most significant potential applications for robots and other artificial autonomous agents. In a typical contest of this sort, one or more pursuers chase one or more evaders around until all preys are captured. Models in which pursuit-evasion problems are examined differ in: environment, number of players, agents’ limitations, definition of capture, optimality criterion, space structure etc. (Isler, Kannan, & Khanna, 2004). Various aspects of pursuit-evasion as well as extensive bibliography on this subject can be found in (Sheppard, 1996).
Key Terms in this Chapter
Stochastic Game: A game, where at any point in time the game is in some state. The game transitions to a new state depend on a stochastic function of the previous state and the interactions among the agents
Directed Mutation: A process that is aimed at creating cells with particular features rather then a random set of cells. First the best cell (in terms of interaction strength with the present antigen) is found and afterwards cells with similar features are created.
Repeated Game: A game consisting of a series of interactions among two or more players. After each interaction players may receive some payoff. Unlike a game played once, a repeated game allows for a strategy to be contingent on past moves
Multi-Agent System: A system consisting of many agents, who can perform tasks individually or co-operate in order to achieve a system goal. A very important factor in such systems are agents’ interaction.Agents’ limitations - anything that prevent agents from acting optimally, e.g. limited perception, limited speed
On-Line Learning: A process, in which a system learns and acts simultaneously
Pursuit-Evasion Game: A game in which, predators, or pursuers, chase preys (evaders) around until the preys are captured. Solution constitutes chasing agents’ optimal strategy, which guarantees execution of their task
Agent: An entity, that perceives its environment and acts upon it in order to realize a given set of goals or tasks
Adaptability: The ability to cope with internal or external changes or to adjust itself to dynamic environments or unexpected events.
Complete Chapter List
Fabio Freschi, Carlos A. Coello Coello, Maurizio Repetto
Jun Chen, Mahdi Mahfouf
Licheng Jiao, Maoguo Gong, Wenping Ma
Malgorzata Lucinska, Slawomir T. Wierzchon
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Krzysztof Ciesielski, Mieczyslaw A. Klopotek, Slawomir T. Wierzchon
Xiangrong Zhang, Fang Liu
Yong-Sheng Ding, Xiang-Feng Zhang, Li-Hong Ren
Alexander O. Tarakanov
Xin Wang, Wenjian Luo, Zhifang Li, Xufa Wang
Mark Burgin, Eugene Eberbach
Terrence P. Fries
Konstantinos Konstantinidis, Georgios Ch. Sirakoulis, Ioannis Andreadis
Miroslav Bursa, Lenka Lhotska
Martin Macaš, Lenka Lhotská
James F. Peters, Shabnam Shahfar
Tang Mo, Wang Kejun, Zhang Jianmin, Zheng Liying