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Sociogenesis in unbounded space: modelling self-organised cohesive collective motion
Zohar Neu1, Luca Giuggioli1,2
1Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TW, United Kingdom.
Physical Biology
|March 17, 2023
Summary
This study introduces a bio-inspired model for collective movement in 1D unbounded space. It uses social ranking to maintain agent cohesion, enabling self-sorting and stable group dynamics.
Area of Science:
- Robotics and Artificial Intelligence
- Complex Systems Science
- Bio-inspired Computing
Background:
- Maintaining agent cohesion in unbounded spaces is crucial for distributed multi-agent systems.
- Existing models often struggle with dynamic environments and decentralized control.
Purpose of the Study:
- To develop a novel bio-inspired collective movement model for 1D unbounded space.
- To ensure system cohesion and stability using agent-internal beliefs and social ranking.
Main Methods:
- A bio-inspired collective movement model in 1D unbounded space was developed.
- Agent motion was coupled to a dynamically self-generated social ranking variable.
- The model's state-space was investigated concerning key control parameters.
Main Results:
- The model induces spatial self-sorting and an adaptive, group-relative coordinate system.
- Two distinct regimes for achieving dynamical cohesive states were identified.
- A Partial Sensing regime was found where agents self-select nearest-neighbor distances.
Conclusions:
- The developed model offers a novel theoretical approach to collective movement.
- Agents making decisions based on internal social environment representations enhance system cohesion.
- This approach accounts for spatial variation in dynamic internal variables for adaptive agent behavior.
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