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Related Experiment Video

Updated: Jun 24, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Cooperative control and potential games.

Jason R Marden¹, Gürdal Arslan, Jeff S Shamma

¹Social and Information Sciences Laboratory,California Institute of Technology, Pasadena, CA 91125, USA. marden@caltech.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|April 17, 2009

Summary

This summary is machine-generated.

This study frames cooperative control problems using game theory, developing new algorithms for agent coordination with limited capabilities. It introduces "sometimes weakly acyclic games" for dynamic scenarios, ensuring stable group decision-making.

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Area of Science:

Control Theory
Game Theory
Multi-agent Systems

Background:

Cooperative control problems like consensus and sensor coverage are crucial in multi-agent systems.
Existing game-theoretic frameworks have limitations in addressing agent capability constraints and group decision-making.

Purpose of the Study:

To unify cooperative control and game theory by leveraging concepts like potential and weakly acyclic games.
To extend learning algorithms for cooperative control with restricted action sets.
To introduce a new game class, 'sometimes weakly acyclic games,' for dynamic environments and develop distributed convergence algorithms.

Main Methods:

Formulating cooperative control problems (consensus, sensor coverage) within potential and weakly acyclic game frameworks.
Extending existing game-theoretic learning algorithms to handle restricted agent action sets.
Introducing and analyzing 'sometimes weakly acyclic games' for time-varying objectives and action sets.
Developing distributed algorithms for achieving equilibrium in these extended game settings.

Main Results:

Demonstrated that consensus and dynamic sensor coverage problems can be modeled using potential and weakly acyclic games.
Successfully extended learning algorithms to accommodate limitations in agent capabilities and group decision-making.
Introduced the 'sometimes weakly acyclic games' class, suitable for dynamic cooperative control scenarios.
Provided distributed algorithms that guarantee convergence to an equilibrium in the proposed game settings.

Conclusions:

Game theory provides a powerful framework for analyzing and solving complex cooperative control problems.
The developed methods and algorithms enhance multi-agent system coordination, particularly under constraints.
The introduction of 'sometimes weakly acyclic games' broadens the applicability of game theory to dynamic and realistic cooperative control challenges.