TY - GEN
T1 - Behaviosites
T2 - 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
AU - Shabtay, Amit
AU - Rabinovich, Zinovi
AU - Rosenschein, Jeffrey S.
PY - 2006
Y1 - 2006
N2 - In this paper we present the Behaviosite Paradigm, a new approach to coordination and control of distributed agents in a multiagent system, inspired by biological parasites with behavior manipulation properties. Behaviosites are code modules that "infect" a system, attaching themselves to agents and altering the sensory activity and actions of those agents. These behavioral changes can be used to achieve altered, potentially improved, performance of the overall system; thus, Behaviosites provide a mechanism for distributed control over a distributed system. Behaviosites need to be designed so that they are intimately familiar with the internal workings of the environment and of the agents operating within it. To demonstrate our approach, we use behaviosites to control the behavior of a swarm of simple agents. With a relatively low infection rate, a few behaviosites can engender desired behavior over the swarm as a whole: keeping it in one place, leading it through checkpoints, or moving the swarm from one stable equilibrium to another. We contrast behaviosites as a distributed swarm control mechanism with alternatives, such as the use of group leaders, herders, or social norms.
AB - In this paper we present the Behaviosite Paradigm, a new approach to coordination and control of distributed agents in a multiagent system, inspired by biological parasites with behavior manipulation properties. Behaviosites are code modules that "infect" a system, attaching themselves to agents and altering the sensory activity and actions of those agents. These behavioral changes can be used to achieve altered, potentially improved, performance of the overall system; thus, Behaviosites provide a mechanism for distributed control over a distributed system. Behaviosites need to be designed so that they are intimately familiar with the internal workings of the environment and of the agents operating within it. To demonstrate our approach, we use behaviosites to control the behavior of a swarm of simple agents. With a relatively low infection rate, a few behaviosites can engender desired behavior over the swarm as a whole: keeping it in one place, leading it through checkpoints, or moving the swarm from one stable equilibrium to another. We contrast behaviosites as a distributed swarm control mechanism with alternatives, such as the use of group leaders, herders, or social norms.
UR - http://www.scopus.com/inward/record.url?scp=33750723434&partnerID=8YFLogxK
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AN - SCOPUS:33750723434
SN - 1577352815
SN - 9781577352815
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 709
EP - 715
BT - Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
Y2 - 16 July 2006 through 20 July 2006
ER -