Our Research
Biological systems, from multicellular organisms to social insects ("superorganisms"), get tremendous mileage from the cooperation of vast numbers of cheap, unreliable, and limited individuals. As we build artificial systems with similar characteristics --- robot swarms, modular robots, sensor networks, programmable materials --- can we achieve the kind of complexity and reliability that nature achieves?
Our group is interested in self-organizing multi-agent systems, where large numbers of simple agents cooperate to produce complex and robust global behavior. We study bio-inspired paradigms for designing and programming collective intelligence in robotics and networks, drawing inspiration mainly from multicellular biology and social insects. We also investigate models of self-organization in biology, specifically how cells and insects cooperate to achieve complex tasks.
A common theme in all of our work is understanding the relationship between local and global behavior: how does robust collective behavior arise from many locally interacting agents, and how can we program the local interations of simple agents to achieve the global behaviors we want.
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