Hybrid multi-agent consensus with game-driven dynamics and connectivity-based partition

Area of Science:

• Control Theory
• Game Theory
• Networked Systems

Background:

• Current hybrid game-based multi-agent systems (HGBMASs) research faces challenges in modularity, topology assumptions, and passive game mechanisms.
• Existing frameworks often verify consensus existence rather than actively designing for it.

Purpose of the Study:

• To introduce an enhanced framework for HGBMASs that integrates topology, game theory, and hierarchical macro-regulation.
• To overcome limitations in modularity, topological assumptions, and game mechanisms in current research.

Main Methods:

• Achieved modular decoupling between game decision and system dynamics layers for engineering flexibility.
• Adapted game mechanisms to topological structures using connectivity-based partitioning (exclusive vs. common parts).
• Developed an enhanced consensus mechanism where agents in common parts converge to convex combinations of exclusive parts, driven by Nash equilibrium.

Main Results:

• Demonstrated modular decoupling for heterogeneous engineering requirements.
• Successfully designed systems for consensus, adapting game mechanisms to network topology.
• Established a closed-loop system where topology guides game design, and the game optimizes topological functionality.

Conclusions:

• The proposed framework effectively solves multi-agent consensus problems in networks with non-negative edge weights.
• The hierarchical structure provides a novel, mechanism-based approach to game regulation.
• The work redefines the design logic of cost functions and Nash equilibrium for global coordination, applicable to systems like air traffic control.