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Collective decision making by embodied neural agents.

Nicolas Coucke1,2,3,4, Mary Katherine Heinrich3, Axel Cleeremans4

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View abstract on PubMed

Summary
This summary is machine-generated.

This study shows how simple neural dynamics and social interactions enable collective decision-making in agents. Successful group choices depend on balancing internal, social, and environmental coordination.

Keywords:
collective decision makingmultiagentneuroAIneurodynamicsswarm intelligence

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Area of Science:

  • Robotics
  • Neuroscience
  • Artificial Intelligence

Background:

  • Collective decision-making is common in multiagent systems like robot swarms.
  • Existing models often neglect neural dynamics crucial for embodied agents' sensorimotor coordination.

Purpose of the Study:

  • Investigate collective decisions driven by sensorimotor coordination in agents with simple neural dynamics.
  • Model agent-environment and inter-agent coordination within a stimulus gradient environment.

Main Methods:

  • Utilized a minimal neural dynamics model based on the coordination dynamics framework.
  • Simulated single-agent and multi-agent setups with stimulus gradients.
  • Analyzed decision-making based on neural dynamics and social interactions.

Main Results:

  • Single-agent decisions relied on neural dynamics and environmental coordination.
  • Multi-agent decisions integrated inter-agent sensorimotor coordination.
  • Collective decision success was contingent on balancing intra-agent, inter-agent, and agent-environment coupling.

Conclusions:

  • Demonstrated how collective behaviors emerge from individual neural dynamics and interactions.
  • Identified environmental factors influencing collective decision difficulty.
  • Highlighted the potential for analyzing collective behaviors through agent neural dynamics, advancing neuro-AI and self-organized systems.