This article provides an overview of multiagent systems, including their architectures, structures, behaviours, problems, advantages, and applications of multi agent systems.
An Introduction to multiagent systems
Multiple artificial intelligence (AI) agents cooperating to carry out activities on behalf of a user or another system make up a multiagent system (MAS).
Although each agent in a MAS has unique qualities, all agents work together to achieve the required global properties. Large-scale, intricate operations that require hundreds or even thousands of agents can be accomplished with the help of multiagent systems.
Artificial intelligence (AI) agents are at the heart of this concept. An artificial intelligence (AI) agent is a software or system that can define its workflow and use the tools at its disposal to carry out tasks on behalf of a user or another system. Large language models (LLMs) are the foundation of AI agents. To understand and react to user inputs, these intelligent agents make use of LLMs’ sophisticated natural language processing techniques. Agents solve issues one step at a time and decide when to use outside resources.
The utilization of tools and the capacity to create an action plan are what set AI agents apart from conventional LLMs. An agent may have access to web searches, application programming interfaces (APIs), and external datasets. AI agents can refresh their memory as they learn new knowledge, just like humans do when making decisions. Compared to classic LLMs, AI agents are more general-purpose due to information sharing, tool utilization, and adaptive learning.
Multi agent system architectures
Centralized networks
Different architectures can be used to operate multiagent systems. A central unit connects the agents, manages their data, and houses the global knowledge base in centralized networks. The uniform knowledge and ease of communication between agents are two advantages of this framework. Dependency on the central unit is a flaw of centrality; if it fails, the entire agent system fails.
Dispersed networks
Instead of exchanging information with a global knowledge base, agents in decentralized networks exchange information with their neighbors. Decentralized networks have several advantages, including modularity and resilience. Since there is no central unit, the failure of one agent does not result in the failure of the entire system. Coordinating actions to help other collaborating agents is one of the challenges faced by decentralized agents.
Multi agent system structures
In a multiagent system, agents can be arranged in a variety of ways, such as:
Hierarchical structure
Agents in a hierarchical framework have different degrees of autonomy and resemble trees. One agent can make all of the decisions in a basic hierarchical system. A homogeneous hierarchical structure allows for the division of responsibilities among several agents.
Holonic structure
Agents are organized into holarchies within this form of architecture. An entity that cannot function without its constituent parts is called a holon. The human body, for example, is a holon as it cannot function without functional organs. Similar to this, the leading agent in holonic multiagent systems may appear to be a single entity while actually having several subagents. These subagents might function in different spheres as well. These self-organizing hierarchical structures are designed to accomplish a task by means of the subagents’ cooperation.
Coalition structure
When a single individual in a group performs poorly, coalitions can be useful. Agents temporarily band together in these circumstances to increase performance or utility. The coalitions are distributed after the targeted performance is achieved. In dynamic situations, it might be challenging to sustain these coalitions. Regrouping is frequently required to improve performance.
Teams
Coalitions and teams share a similar structure. Agents work together in teams to enhance the group’s performance. Unlike in coalitions, agents in teams do not operate alone. Teams have a more hierarchical structure and agents who are far more reliant on one another than coalitions.
Multi agent system behaviors
A multiagent system’s agent behaviors frequently mimic those found in the natural world. Both multi software and multirobot agents can exhibit the same behaviors.
Flocking
Multiagent systems can exhibit collective behavior that is similar to that of humans, fish, and birds. Agents in these systems have a common goal and need some structure to coordinate their actions. Directional synchronization is related to flocking, and the following heuristics can be used to characterize the structure of these flocks:
- Separation: try to stay clear of agents that are close by.
- Alignment: try to match the agents’ velocities in the vicinity.
- Cohesion: make an effort to stay near other agents.
This coordination is essential for multiagent systems that manage transportation networks, such railway systems, in the context of software agents.
Swarming
Swarming in nature can be compared to the spatial arrangement of agents in a multiagent system. For example, birds adapt to their surroundings to fly in unison. Swarming is defined technically as the emergence of self-organization and aggregation among decentralized software agents. Swarming has the advantage of allowing a single operator to be trained to control a large number of agents. Compared to training an operator for every agent, this approach is more dependable and requires less computing power.
Applications Of Multi Agent Systems
Numerous challenging real-world problems can be resolved by multiagent systems. The following are a few instances of relevant domains:
Transportation
Transportation systems can be managed by multiagent systems. Communication, cooperation, planning, and real-time information access are characteristics of multiagent systems that enable the coordination of intricate transportation networks. Railroad systems, truck assignments, and marine boats that visit the same ports are a few examples of dispersed systems that could profit from MAS.
Public health and healthcare
In the healthcare industry, multiagent systems are useful for a variety of specialised jobs. By using genetic analysis, these agent-based systems can help anticipate and prevent disease. One use could be in medical studies on cancer. Multiagent systems can also be used to simulate and prevent the spread of epidemics. Neural networks using epidemiological knowledge and machine learning (ML) approaches to handle massive datasets enable this predicting. Public policy and public health may be impacted by these findings.
Supply chain management
A supply chain is influenced by many factors. These elements include everything from the production of goods to the purchase made by the customer. Multiagent systems can link the elements of supply chain management by utilising their extensive data resources, adaptability, and scalability. Virtual agents should bargain with one other to find the best way through this clever automation. When agents are working with other agents who have different objectives, this negotiation is crucial.
Defense systems
Defence systems can be strengthened with the help of multiagent systems. Cyberattacks and actual national security problems are examples of potential threats. Multiagent systems can mimic possible threats by using their tools. Simulations of maritime attacks are one example. In this scenario, agents would collaborate in groups to document the exchanges between defence vessels and oncoming terrorist boats. Additionally, by collaborating in groups, agents can keep an eye on various network segments to identify potential dangers like distributed denial of service (DDoS) flooding attacks.
Multi agent systems’ benefits
There are a number of benefits associated with multi agent systems, including:
- Flexibility: Agents can be added, removed, or modified in multi agent systems to adapt to changing circumstances.
- Scalability: A larger pool of shared information is made possible by multiple agents working together. Because of this cooperation, multiagent systems are able to tackle more difficult tasks and issues than single-agent systems.
- Domain specialization: While each agent in a multiagent system can possess specialised domain knowledge, single agent systems require a single agent to carry out tasks across several domains.
Greater performance
Singular agents typically perform worse than multiagent frameworks. This is due to the fact that learning and reflection happen more when an agent has access to more action plans. Information synthesis can benefit from an AI agent that integrates input and expertise from other AI agents with specialised fields. Agentic frameworks are a potent tool and a significant development in artificial intelligence because of their capacity to bridge knowledge gaps and collaborate with AI agents on the backend.
Multi agent system challenges
The design and implementation of multiagent systems provide a number of difficulties, such as:
Agent errors
When multiagent systems are constructed using the same foundation models, they may encounter common problems. Such flaws could expose the system to harmful attacks or result in the failure of all agents engaged. This emphasizes the necessity of rigorous training and testing procedures as well as the significance of data governance in creating foundation models.
Coordination complexity
Creating agents with the ability to coordinate and negotiate with one another is one of the most difficult aspects of creating multiagent systems. For a multiagent system to work, this collaboration is necessary.
Unpredictable behavior
In decentralized networks, agents operating freely and autonomously may exhibit contradictory or erratic behavior. It may be challenging to identify and handle problems in the broader system in these circumstances.
