Source: Human-Machine and Cognitive LaboratoryIn complex dynamic game environments, collaborative control and decision-making among multiple agents is often a highly challenging problem, involving theories and technologies from various fields such as game theory, control theory, multi-agent systems, and machine learning. The goal of multiple agents is typically to optimize collective benefits while addressing conflicts and cooperation among individuals. This collaborative control and decision-making problem can be viewed as a process where multiple agents make strategic choices and engage in games in a dynamic environment, where each agent is influenced by the behaviors of other agents while also having its own objectives.1. Basics of Game Theory and Collective IntelligenceGame theory provides a mathematical framework for studying interactions among multiple agents, with common game models including static and dynamic games. In dynamic games, an agent’s decisions are influenced not only by the current state but also by historical decisions and future expectations. Specifically, regarding the collaborative control and decision-making of collective intelligent agents, the core issues in game theory include: 1) Nash Equilibrium: In game theory, Nash equilibrium refers to a state in a game where the strategy combination of all agents has reached a stable state in some sense, meaning no agent can achieve a better outcome by unilaterally changing its strategy. In collaborative control of collective intelligent agents, Nash equilibrium typically implies that agents have chosen an optimal strategy considering the strategies of other agents. 2) Repeated Games: In complex dynamic environments, agents’ decisions are often dynamic, involving future strategy choices and the influence of past strategies. The repeated game model considers multiple iterations of the game and the long-term cooperation or competition among agents. 3) Evolutionary Games: Evolutionary game theory borrows from the evolutionary mechanisms in biology, where agents gradually ‘evolve’ more efficient decision-making methods based on successful strategies in the game. In the collaborative control of collective intelligent agents, evolutionary games can explain how agents find long-term stable strategies through continuous interaction and adaptation.2. Collaborative Control of Collective Intelligent AgentsCollaborative control of collective intelligent agents involves how multiple agents efficiently allocate tasks, coordinate actions, and respond to potential conflicts during task completion. Key technologies and methods include: 1) Distributed Control: Each agent relies only on local information and makes decisions based on the states of other agents. Distributed control methods are commonly used in environments where complete global information is not available, with typical control strategies including consensus control, consensus control, and optimal control. 2) Cooperative Evolutionary Algorithms: Utilizing the cooperation and competition mechanisms of collective intelligent agents, these algorithms optimize decisions and actions by simulating cooperation and competition among species in nature. These algorithms include Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), etc., which have wide applications in collaborative control of collective intelligent agents. 3) Collaborative Frameworks for Multi-Agent Systems: These frameworks involve completing tasks through collective behavior, such as fleet coordination in autonomous driving and formation control of drone swarms. In these scenarios, collective intelligent agents need to find a balance between sharing information and making local decisions to achieve common goals.3. Challenges in Complex Dynamic EnvironmentsIn complex dynamic game environments, collaborative control and decision-making of collective intelligent agents face unique challenges: 1) Incomplete Information and Information Asymmetry: In most real-world environments, each agent typically only has access to local information and cannot fully grasp global information. This leads to uncertainty in the decision-making process of the game, and how to make optimal decisions under incomplete information is a key issue. 2) Time-Varying Environments and Dynamic Adaptation: Complex environments are often dynamically changing, and agents must be capable of real-time adaptation to environmental changes. In applications such as traffic management and smart grids, changes in the environment (e.g., traffic conditions, energy demand) affect agents’ decisions, and how to respond quickly and adjust strategies is a challenge. 3) Balancing Conflict and Cooperation: In multi-agent systems, there may be conflicts between individual goals and collective goals. How to balance the contradictions between individuals and the collective through game theory models and ensure overall system efficiency is an important issue in designing effective decision-making and control systems. 4) Computational Complexity and Real-Time Requirements: In large-scale collective intelligent agent systems, computational complexity is often a significant issue. Real-time requirements mean that agents must make decisions within limited time, and how to design efficient algorithms to handle real-time decision-making in large-scale, complex environments remains an urgent problem.4. Applications of Collaborative Control of Collective Intelligent AgentsCollaborative control of collective intelligent agents has significant implications in various practical applications: 1) Drone Swarms: The collaborative control of drone swarms has widespread applications in military, disaster rescue, environmental monitoring, and other fields. Drones need to coordinate and avoid obstacles without complete global information to achieve collective goals. 2) Autonomous Driving and Intelligent Traffic Systems: Multiple autonomous vehicles need to collaboratively control themselves in complex traffic environments. How to achieve intelligent decision-making and cooperation among vehicles through game theory models under incomplete information, avoid traffic accidents, and improve traffic efficiency is an important direction of current research. 3) Smart Grids and Energy Scheduling: In smart grids, various agents (e.g., power companies, power plants, users) need to make collaborative decisions to optimize energy distribution and scheduling. In such systems, the behavior of each agent affects the overall stability and efficiency of the grid, and how to design game models to optimize scheduling strategies is a key issue. Collaborative control and decision-making of multiple agents in complex dynamic game environments is a highly interdisciplinary and challenging research field. With advances in technology, especially in the deep integration of artificial intelligence, machine learning, and game theory, more complex system problems can be effectively solved through the collaboration and decision-making of collective intelligent agents. By continuously improving algorithms and models, and through ongoing validation in practical applications, the prospects for the application of collective intelligent agents across multiple fields are broad, providing effective solutions to many complex problems in the real world.【Disclaimer】This article is reproduced for non-commercial educational and research purposes only, for the dissemination of academic news information. Copyright belongs to the original author. If there is any infringement, please contact us immediately, and we will delete it in a timely manner.
