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Multi-agent systems consist of autonomous agents that work together to solve complex problems. These agents communicate and negotiate to achieve shared goals, such as optimizing resource allocation and managing dynamic systems. This capability enables effective coordination in various fields, including logistics, robotics, and smart grids.
In multi-agent systems, decision-making is decentralized. Each agent makes independent decisions based on its local knowledge and the surrounding environment. This approach enhances scalability and fault tolerance, making multi-agent systems suitable for large-scale applications like traffic management and distributed computing.
Multi-agent systems offer a framework for simulating and modeling complex systems involving multiple interacting entities. Applications include studying social behaviors, economic systems, and environmental changes. Agent-based modeling helps predict outcomes and analyze various scenarios.
Agents in multi-agent systems can employ machine learning techniques to adapt to dynamic environments and improve their performance over time. For instance, reinforcement learning enables agents to develop strategies for collaboration, conflict resolution, and achieving both individual and collective goals.
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