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Multi-Agent AI Systems: Architectures and Collaboration
- Agent Architecture: Each agent is defined by a tuple `a = {m, o, e, x, y}`, where `m` is the model (architecture, memory, adapters), `o` is the objective, `e` is the environment, `x` is the input perception, and `y` is the output action (`y = m(o, e, x)`), enabling specialized roles and scalable workflows.
- System Architecture: A multi-agent system (MAS) is defined as `y_collab = S(O_collab, E, x_collab | A, C)`, where `O_collab` is the shared goal, `E` is the environment, `A` is the set of agents, and `C` represents collaboration channels that dictate agent interaction, planning, and action.
- Collaboration Types: MAS can implement cooperation (shared goals), competition (individual goals), or coopetition (mixed goals), influencing system robustness and adaptability; for example, cooperation involves aligning objectives `O_collab = _i=1_[o][i]`.
- Collaboration Strategies: Strategies include rule-based (strict logic), role-based (predefined jobs), and model-based (probabilistic planning), each offering trade-offs in adaptability and complexity; model-based approaches adapt to dynamic environments but are computationally expensive.
- Communication Topologies: Communication can be centralized (hub-and-spoke), decentralized (peer-to-peer), or hierarchical (layered control), affecting system flexibility and fragility; decentralized structures offer greater flexibility but increase complexity.
- Real-World Applications: MAS are applied in QA (utilizing debate frameworks like MAD/FORD), software development (simulating agile teams via tools like ChatDev/MetaGPT), and IoT (coordinating edge devices), demonstrating their versatility. According to additional sources, MAS are also used in 5G/6G networks (LLM-SC, LaMoSC, LAM-MSC, GMAC), question answering (OpenAI Swarm, Microsoft Magentic-One, IBM Bee Agent Framework, LangChain Agents), and social/cultural domains (CulturePark).
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