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Informational Memory Shapes Collective Behavior in Intelligent Swarms.

Shengkai Li1, Trung V Phan2, Luca Di Carlo1

  • 1Princeton University, Department of Physics, Princeton, New Jersey 08544, USA.

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We studied 2-D drone swarms where agents process information from past collisions. This memory-driven computation leads to emergent collective behaviors and phase transitions in spin symmetry.

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Area of Science:

  • Physics of complex systems
  • Robotics and autonomous systems
  • Information theory and computation

Background:

  • Collective behavior in swarms often relies on direct interactions.
  • The role of internal agent memory and information processing in collective organization is less understood.
  • Understanding how information dynamics influence emergent phenomena is crucial.

Purpose of the Study:

  • To investigate how agent memory and information processing drive collective behavior in 2-D swarms.
  • To explore the emergence of novel collective states and phase transitions.
  • To develop a theoretical framework linking information processing to swarm organization.

Main Methods:

  • Experimental study of air-table drones with internal memory and binary decision variables.
  • Theoretical modeling using an effective potential landscape approach.
  • Analysis of history-dependent feedback and its impact on collective dynamics.

Main Results:

  • Observed spontaneous spin polarization and bifurcated spin collectives.
  • Discovered a memory-induced phase transition that breaks population-level spin symmetry.
  • Demonstrated that temporally correlated computation can drive collective organization.

Conclusions:

  • Physically interacting drone swarms serve as a model for informational ensembles.
  • Emergent behavior arises from the interplay of physical interaction and information processing.
  • Information processing can act as a primary driver for collective organization, complementing mechanical coupling.