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


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.

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This paper was partially supported by a Grant-in-Aid for Scientific Research on Innovative Areas (Research Project Number: 15H01612). This paper was partially supported by JST, CREST (Research Area: Advanced Core Technologies for Big Data Integration).

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Correspondence to Aleksandr Drozd.

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This work was presented in part at the 1st International Symposium on Swarm Behavior and Bio-Inspired Robotics, Kyoto, Japan, October 28–30, 2015.

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Drozd, A., Witkowski, O., Matsuoka, S. et al. Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments. Artif Life Robotics 21, 317–323 (2016).

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  • Artificial life
  • Artificial neural networks
  • Bio-inspired computation
  • Evolutionary robotics
  • Foraging
  • Swarming
  • Treecode