Biologically-Inspired Control for Multi-Agent Self-Adaptive Tasks

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Biologically-Inspired Control for Multi-Agent Self-Adaptive Tasks

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dc.contributor.author Yu, Chih-Han
dc.contributor.author Nagpal, Radhika
dc.date.accessioned 2012-11-28T19:39:15Z
dc.date.issued 2010
dc.identifier.citation Yu, Chih-Han, and Radhika Nagpal. 2010. Biologically-inspired control for multi-agent self-adaptive tasks. In Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference: July 11-15, 2010, Atlanta, Georgia, 1702-1709. Menlo Park, CA: American Association for Artificial Intelligence Press. en_US
dc.identifier.isbn 978-1-57735-463-5 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:9962004
dc.description.abstract Decentralized agent groups typically require complex mechanisms to accomplish coordinated tasks. In contrast, biological systems can achieve intelligent group behaviors with each agent performing simple sensing and actions. We summarize our recent papers on a biologically-inspired control framework for multi-agent tasks that is based on a simple and iterative control law. We theoretically analyze important aspects of this decentralized approach, such as the convergence and scalability, and further demonstrate how this approach applies to real-world applications with a diverse set of multi-agent applications. These results provide a deeper understanding of the contrast between centralized and decentralized algorithms in multi-agent tasks and autonomous robot control. en_US
dc.description.sponsorship Engineering and Applied Sciences en_US
dc.description.sponsorship Other Research Unit en_US
dc.language.iso en_US en_US
dc.publisher American Association for Artificial Intelligence (AAAI) Press en_US
dc.relation.isversionof http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/viewFile/1755/2285 en_US
dc.relation.hasversion http://www.eecs.harvard.edu/ssr/papers/aaai10-yu.pdf en_US
dc.relation.hasversion http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.184.3637&rep=rep1&type=pdf en_US
dash.license OAP
dc.title Biologically-Inspired Control for Multi-Agent Self-Adaptive Tasks en_US
dc.type Monograph or Book en_US
dc.description.version Author's Original en_US
dash.depositing.author Nagpal, Radhika
dc.date.available 2012-11-28T19:39:15Z

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  • FAS Scholarly Articles [7218]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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