Adaptive Multi Agent Collaboration Frameworks for Distributed Problem Solving in Artificial Intelligence
Keywords:
Multi-Agent Systems (MAS), Adaptive Collaboration, Distributed Problem Solving, Swarm Intelligence, Reinforcement Learning, Negotiation Strategies, Artificial Intelligence Coordination, ScalabilitySynopsis
This paper explores adaptive multi-agent collaboration frameworks for distributed problem solving in artificial intelligence (AI). The study emphasizes the role of adaptability, coordination mechanisms, and learning-based strategies in enhancing the efficiency of distributed problem-solving. By analyzing swarm intelligence, reinforcement learning, and negotiation-based agent interactions, we propose a reference architecture for scalable and resilient collaboration. Our work integrates these findings into a conceptual model, supported by mind maps, architecture diagrams, and workflow sequences, to visualize how agents collectively adapt to dynamic environments.
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