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

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

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

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