Leader Selection via Supermodular Game for Formation Control in Multiagent Systems.

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Game Theory

Background:

• Multiagent systems (MASs) often employ leader-follower structures for formation control.
• Effective leader selection is critical for minimizing formation errors in MAS applications.
• Existing methods for leader selection in MASs require improvement for optimal performance.

Purpose of the Study:

• To address two key leader selection problems in second-order MASs.
• To minimize formation error by selecting an optimal number of leaders.
• To achieve a tolerated error level with the minimum number of leaders.

Main Methods:

• Development of a game theoretical framework for leader selection problems.
• Design and theoretical proof of supermodularity for the proposed game.
• Implementation of stochastic fictitious play for agents to learn leader selection and reach Nash equilibrium.

Main Results:

• The proposed game-theoretical method effectively addresses leader selection in second-order MASs.
• Stochastic fictitious play enables agents to learn optimal leader selection strategies.
• Simulation results show superior performance compared to existing leader selection methods.

Conclusions:

• The game theoretical approach provides a robust solution for leader selection in MASs.
• The proposed method enhances formation control by optimizing leader selection.
• This research offers a novel and effective strategy for MAS leader selection.