Quantifying long-range interactions and coherent structure in multi-agent dynamics

Oliver M. Cliff*, Joseph T. Lizier, X. Rosalind Wang, Peter Wang, Oliver Obst, Mikhail Prokopenko

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


We develop and apply several novel methods quantifying dynamic multi-agent team interactions. These interactions are detected information-theoretically and captured in two ways: via (i) directed networks (interaction diagrams) representing significant coupled dynamics between pairs of agents, and (ii) state-space plots (coherence diagrams) showing coherent structures in Shannon information dynamics. This model-free analysis relates, on the one hand, the information transfer to responsiveness of the agents and the team, and, on the other hand, the information storage within the team to the team's rigidity and lack of tactical flexibility. The resultant interaction and coherence diagrams reveal implicit interactions, across teams, that may be spatially long-range. The analysis was verified with a statistically significant number of experiments (using simulated football games, produced during RoboCup 2D Simulation League matches), identifying the zones of the most intense competition, the extent and types of interactions, and the correlation between the strength of specific interactions and the results of the matches.

Original languageEnglish
Pages (from-to)34-57
Number of pages24
JournalArtificial Life
Issue number1
Publication statusPublished - 2017
Externally publishedYes


  • Multi-agent dynamics
  • distributed computation
  • implicit communication
  • information storage
  • information transfer
  • Implicit communication
  • Information transfer
  • Information storage
  • Distributed computation


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