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Guiding Self-Organization in Systems of Cooperative Mobile Agents

Guiding Self-Organization in Systems of Cooperative Mobile Agents

Alejandro Rodríguez, Alexander Grushin, James A. Reggia
Copyright: © 2009 |Pages: 23
ISBN13: 9781599049960|ISBN10: 1599049961|EISBN13: 9781599049977
DOI: 10.4018/978-1-59904-996-0.ch015
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MLA

Rodríguez, Alejandro, et al. "Guiding Self-Organization in Systems of Cooperative Mobile Agents." Advancing Artificial Intelligence through Biological Process Applications, edited by Ana B. Porto Pazos, et al., IGI Global, 2009, pp. 268-290. https://doi.org/10.4018/978-1-59904-996-0.ch015

APA

Rodríguez, A., Grushin, A., & Reggia, J. A. (2009). Guiding Self-Organization in Systems of Cooperative Mobile Agents. In A. Porto Pazos, A. Pazos Sierra, & W. Buño Buceta (Eds.), Advancing Artificial Intelligence through Biological Process Applications (pp. 268-290). IGI Global. https://doi.org/10.4018/978-1-59904-996-0.ch015

Chicago

Rodríguez, Alejandro, Alexander Grushin, and James A. Reggia. "Guiding Self-Organization in Systems of Cooperative Mobile Agents." In Advancing Artificial Intelligence through Biological Process Applications, edited by Ana B. Porto Pazos, Alejandro Pazos Sierra, and Washington Buño Buceta, 268-290. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-996-0.ch015

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Abstract

Drawing inspiration from social interactions in nature, swarm intelligence has presented a promising approach to the design of complex systems consisting of numerous, simple parts, to solve a wide variety of problems. Swarm intelligence systems involve highly parallel computations across space, based heavily on the emergence of global behavior through local interactions of components. This has a disadvantage as the desired behavior of a system becomes hard to predict or design. Here we describe how to provide greater control over swarm intelligence systems, and potentially more useful goal-oriented behavior, by introducing hierarchical controllers in the components. This allows each particle-like controller to extend its reactive behavior in a more goal-oriented style, while keeping the locality of the interactions. We present three systems designed using this approach: a competitive foraging system, a system for the collective transport and distribution of goods, and a self-assembly system capable of creating complex 3D structures. Our results show that it is possible to guide the self-organization process at different levels of the designated task, suggesting that self-organizing behavior may be extensible to support problem solving in various contexts.

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