Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

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• 作者：Aleksandr Drozd ; Olaf Witkowski ; Satoshi Matsuoka…
• 关键词：Artificial life ; Artificial neural networks ; Bio ; inspired computation ; Evolutionary robotics ; Foraging ; Swarming ; Treecode
• 刊名：Artificial Life and Robotics
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：21
• 期：3
• 页码：317-323
• 全文大小：836 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computation by Abstract Devices
  Automation and Robotics
  
• 出版者：Springer Japan
• ISSN：1614-7456
• 卷排序：21

文摘

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space.