Digital twins have emerged as transformative tools for simulating and monitoring complex systems across industries such as manufacturing, healthcare, and smart cities. However, as the scale and complexity of these ecosystems grow, traditional single-model digital twins face limitations in adaptability, scalability, and decision-making.
This paper explores the integration of Multi-Agent Systems (MAS) with Digital Twin architectures to create collaborative, autonomous AI models capable of dynamic interaction and real-time optimization. We propose a conceptual framework in which digital twins function as intelligent agents that cooperate, negotiate, and adapt within larger ecosystems.
Key challenges such as agent communication, conflict resolution, distributed learning, and ethical considerations are analyzed. Furthermore, we illustrate the potential of MAS-driven digital twins through case studies in manufacturing supply chains and smart energy grids.
By fusing the strengths of digital twins and multi-agent systems, this research lays the groundwork for next-generation resilient, decentralized, and self-organizing cyber-physical systems.
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https://www.researchgate.net/publication/390821102_Exploring_the_Synergies_of_Multi-Agent_Systems_and_Digital_Twins
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