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Developing collaborative autonomous agents that can learn and evolve, investigating methods for engineering emergent collective behavior in large agent societies and building micro-nano-scale tangible agents to implement proof-of-concept prototypes

This project aims to design and develop teams of co-operating autonomous agents, which could generally be used to expand the action-horizon of humans in inaccessible industrial fluidic applications. Successful collective performance in these missions critically depends upon: (a) accurate environmental perception, (b) a real time decision making and action loop, (c) emergent goal directed social behavior. In order to meet these requirements, we:

  • Propose a novel agent architectural design based on modular Spiking Neural Network, which will serve as a "blueprint" for agent manufacture
  • Research direct/indirect communication mechanisms for collaborative societies formation, and agent to environment communication.
  • Design and develop an accompanying Development Environment, which will facilitate the evolution of complex agent SNN architectures- multiagent societies, and the development of successive CAA generations.

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