Description: Multi-Agent Systems are architectures composed of multiple intelligent agents that interact with each other to solve complex problems. Each agent is an autonomous entity that can perceive its environment, reason about the information it receives, and make decisions based on its goals and the available information. These systems are characterized by their ability to collaborate, communicate, and negotiate among agents, allowing them to tackle tasks that would be difficult or impossible for a single agent to perform. In the context of Industry 4.0, Multi-Agent Systems are essential for the automation and optimization of industrial processes, as they enable the coordination of machines, robots, and other connected devices. In the field of Natural Language Processing, these systems can facilitate interaction between users and applications through conversational agents that understand and respond to queries in natural language. In AI Automation, Multi-Agent Systems can manage and optimize workflows, improving operational efficiency. Finally, in Edge Computing, these systems allow agents to process data locally, reducing latency and enhancing real-time decision-making.
History: Multi-Agent Systems began to develop in the 1990s, although their roots can be traced back to game theory and artificial intelligence. One significant milestone was the creation of platforms like JADE (Java Agent Development Framework) in 1999, which facilitated the development of agent-based applications. Over the years, research in this field has grown, addressing topics such as agent cooperation, conflict resolution, and effective communication.
Uses: Multi-Agent Systems are used in various areas, including robotics, where they enable the coordination of multiple robots in complex tasks. They are also applied in resource management in sensor networks, in intelligent transportation systems, and in the simulation of social and economic phenomena. Additionally, they are useful in the development of virtual assistants and chatbots that interact with users.
Examples: A practical example of Multi-Agent Systems is the use of robots on a production line, where each robot acts as an agent that communicates and collaborates with others to optimize product assembly. Another example is the use of agents in e-commerce platforms that manage inventories and prices in real-time, adjusting to market demand.
