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Overview
The overarching goal of this MURI proposal is to develop and test experimental, simulation and theoretical frameworks within which to discover the fundamental principles that would allow one to understand, enable, and synthesize emergent behavior in self-organizing systems.
Self-organization can be viewed as a phenomenon whereby unanticipated global configurations and patterns of activity of a collective emerge from fully distributed and simplistic rules followed by each individual, without any global coordination or external intervention.
Self-organization and emergent behavior arise naturally across many fields:
modeling distributed systems and swarm robotics in computer science, nonequilibrium and equilibrium systems in physics, population
dynamics in biology, autonomous systems in robotics and control theory, and smart materials, to name a few.
We are interested in the
emergent properties of an ensemble of particles that can be predicted and potentially synthesized through analysis and design of algorithmic and physical properties of each individual particle. The ability of particles to encode rules that --- through local sensing, local actuation, memory, and simple operations --- create ensemble-level structural capabilities is a key property of emergent algorithmic behavior; we will analytically relate ensemble structure to the finite states available to individuals.
This approach leads us to the notion of algorithmic (active) matter: Ensembles of particles that interact locally leveraging their physical characteristics and their interaction with the environment, using limited computational resources, bounded communication, and bounded memory to achieve complex tasks.
Specifically, we plan to address how to:
- predict physical and computational requirements for emergent computation,
- determine what non-equilibrium characteristics cause these systems to evolve towards the desired emergent behavior,
- design efficient collective computational systems to achieve specific task-oriented goals.
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