Biologically-Inspired Control for Multi-Agent Self-Adaptive Tasks
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CitationYu, 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.
AbstractDecentralized 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:9962004
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