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The increasing need for complex interactions and automated decision-making in multidimensional environments has led to the emergence of Multi-Agent Systems (MAS) in the field of artificial intelligence. In this article, FPT.AI delves into the workings, types, and practical applications of Multi-Agent Systems across various fields.
What is a Multi Agent System?
A Multi Agent System (MAS) is a distributed model comprising multiple AI Agents working together to accomplish complex tasks and achieve common goals. Key characteristics of an MAS include:
- High Autonomy: Each AI Agent operates independently, with the ability to plan, make decisions, and perform necessary actions to achieve its objectives. This autonomy enhances system flexibility and automation while reducing the need for manual oversight.
- Collaboration and Information Sharing: The agents do not work in isolation but coordinate as an intelligent collaborative network. This cooperation amplifies collective intelligence, enabling more accurate and faster decision-making compared to standalone AI Agents.
- Scalability and Flexibility: The system can easily add or remove AI Agents without disrupting overall operations, making MAS ideal for dynamic and complex environments with frequently changing requirements.
- Fault Tolerance and Reliability: Due to its distributed nature, when one AI Agent encounters an issue, others can continue functioning normally, ensuring system continuity and stability.
- Learning and Adaptation: MAS can learn from experience and adjust behavior to environmental changes. AI Agents share their learning, allowing the system to quickly adapt and improve response capabilities.
| Aspect | Single Agent System | Multi Agent System |
| Structure | Contains a single AI Agent. | Comprises multiple AI Agents interacting and coordinating with one another. |
| System Complexity | Simple with minimal interaction. | More complex, requiring interaction and coordination among agents. |
| Coordination | No coordination needed as there is only one agent. | Agents coordinate and share information to solve tasks. |
| Scalability | Limited; difficult to scale as it relies on a single agent. | Flexible; easily scalable by adding new agents. |
| Fault Tolerance | Susceptible to interruptions if the sole agent fails. | System remains operational even if some agents fail. |
| Decision-Making | Decisions are made by the single agent. | Distributed decision-making based on shared information among agents. |
| Communication | No communication between agents since there is only one. | Continuous communication and information sharing between agents. |
| Adaptability | Limited, reliant on the single agent’s capabilities. | Highly adaptable through shared learning and collective experience. |
| Application Scenarios | Suitable for simple tasks with low interaction requirements. | Ideal for complex, multidimensional tasks requiring collaboration. |
In summary, Multi Agent Systems surpass Single-Agent Systems in scalability, flexibility, and fault tolerance. Their superior performance makes MAS the optimal solution for complex applications requiring distributed computing.
>>> Explore: What Are Intelligent Agents? The Difference Between AI Agents and Intelligent Agents
Types of Multi-Agent Systems
MAS can be categorized based on how agents interact and coordinate:
Cooperative Agent
These systems involve agents working collaboratively, sharing information continuously to achieve a common goal. For instance, in a customer support chatbot system utilizing large language models (LLMs), one agent handles user queries, another retrieves information from the database, and a third synthesizes responses. This collaboration ensures quick and accurate feedback for users.
Adversarial Agent
In these systems, agents have conflicting goals and often use strategies like game theory to predict and counter opponents’ actions. For example, in chess, each AI Agent acts as an independent player, analyzing the board, predicting the opponent’s moves, and adjusting strategies to win. MAS in chess may collaborate to evaluate potential moves, experiment with strategies, or compete to identify the optimal strategy for success.
Mixed-Agent
These systems reflect real-world complexity, where agents cooperate and compete simultaneously. They may negotiate and form temporary alliances to achieve shared benefits, but later compete for individual objectives. An illustration of this is multiple LLM-based agents work together to develop a cohesive narrative on a collaborative storytelling platform. While ensuring consistency in plot and characters, they compete to contribute the most creative ideas, such as surprising plot twists or memorable dialogues, to captivate the audience.
>>> Read more about: What is an LLM Agent? How it works, advantages, and disadvantages
Hierarchical Agents
Hierarchical Agents are organized in a tiered structure, where higher-level AI Agents manage and coordinate lower-level AI Agents. This structure enables efficient task management and delegation, with higher-level agents overseeing operations while lower-level agents execute specific tasks.
In a content management system, a higher-level Supervisor Agent oversees the entire process, delegating tasks to lower-level agents: one agent specializes in information retrieval, another focuses on writing articles, and a final agent handles editing. This coordination ensures a seamless process aligned with overarching strategies.
Heterogeneous Agents
Heterogeneous Agents consist of AI Agents with different capabilities, skills, and roles. This diversity allows each agent to undertake specific tasks based on its unique competencies, resulting in a flexible and highly adaptable system.
In a comprehensive customer support system, AI Agents with different specialties work together: one agent addresses technical issues, another handles payment-related inquiries, and a third provides product recommendations. This diversity ensures a swift and holistic response to customer needs.
Practical Applications of Multi Agent Systems
Multi-Agent Systems (MAS) are widely applied across various fields due to their ability to interact, learn, and make autonomous decisions. Key applications include:
- Energy Sector: MAS is effectively utilized in smart grids for managing electricity distribution, coordinating energy sources, and predicting consumption needs. Real-time data analysis and learning capabilities optimize renewable energy usage and minimize blackouts. Coordination among agents ensures grid stability and demand-responsive adjustments.
- Disaster Relief and Rescue: Autonomous robots equipped with MAS can collaborate to map affected areas, locate victims, and deliver relief supplies. Interaction and information sharing among agents accelerate search efforts, reduce risks, and enhance rescue efficiency.
- Manufacturing: MAS is deployed to monitor processes from quality control to product packaging. It optimizes production, reduces errors, and improves product quality. MAS plays a core role in large-scale industries like automobile manufacturing, driving automation and boosting productivity.
Challenges in Implementing Multi-Agent Systems
Despite their advantages, deploying MAS in real-world scenarios presents significant challenges:
- Coordination Complexity: Collaboration among agents with differing goals requires mechanisms to ensure synchronization and prevent conflicts.
- Unpredictable Behavior: Autonomous agents may exhibit unexpected behaviors, especially in decentralized systems, complicating control and stability.
- Dependence on Foundation Models: MAS often relies on shared foundational models. Errors in these models can disrupt the entire system or expose vulnerabilities.
- High Computational Resources: Maintaining and operating MAS demands substantial computational resources, including data processing, memory, and network bandwidth. This is particularly challenging in resource-constrained or real-time environments.
- Security and Cyber Threats: MAS is susceptible to cyberattacks such as Distributed Denial of Service (DDoS) or tampering with communication channels among agents.
- Standardizing Communication: Agents in MAS may use different protocols or languages, complicating the establishment of standardized communication systems.
- Testing and Evaluation: Ensuring system safety, simulating possible scenarios, and evaluating MAS performance in real environments is highly complex, especially for unforeseen situations.
- Scalability and Management: As system size grows, the number of agents increases, complicating management and maintaining performance.
- Ethical and Accountability Issues: As agents become more autonomous, ethical and accountability concerns rise. Determining responsibility when MAS makes errors or causes unintended consequences must be carefully considered during design and deployment.
A leading supplier of MAS in Vietnam
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