Heterogeneous Multiagent Task Allocation via Cooperative Exchange Strategies for Equilibrium-Improving: A Potential...

Area of Science:

• Multiagent Systems
• Game Theory
• Operations Research

Background:

• Task allocation in multiagent systems is complex due to agent and task heterogeneity.
• Resource depletion and dynamic task requirements add further challenges.
• Applications include optimizing UAV deployment for disaster rescue.

Purpose of the Study:

• To propose a coalition formation game model for efficient task allocation.
• To theoretically analyze the game's properties and efficiency bounds.
• To develop a learning algorithm for improved global utility and escaping suboptimal equilibria.

Main Methods:

• Formulation of a coalition formation game as a potential game.
• Theoretical proof of submodularity for coalition and global utility functions.
• Development of an inertia-based log-linear learning algorithm with cooperative exchange.
• Extension of the algorithm for local communication and dynamic scenarios.

Main Results:

• Theoretical efficiency lower bound of $e / (2e-1)$ for Nash equilibria, surpassing prior 50% bounds.
• The learning algorithm effectively escapes suboptimal equilibria and enhances global utility.
• Demonstrated superior performance across diverse scenarios in experimental evaluations.

Conclusions:

• The proposed coalition formation game and learning algorithm offer a significant advancement in multiagent task allocation.
• The theoretical framework guarantees improved efficiency bounds.
• The practical algorithm demonstrates robustness and effectiveness in complex, dynamic environments.