用多智能体强化学习实现自动协商与共识，提升复杂环境下的协作效率。
Dialogue Diplomats: An End-to-End Multi-Agent Reinforcement Learning System for Automated Conflict Resolution and Consensus Building
- 构建分层共识网络，融合注意力与图神经网络建模智能体间关系。
- 设计渐进式谈判协议，支持多轮对话与动态让步策略。
- 引入上下文感知奖励机制，平衡个体目标与集体共识。
冲突解决与共识构建是多智能体系统、谈判及协作决策中的关键挑战。本文提出Dialogue Diplomats，一种端到端的多智能体强化学习框架，用于在复杂动态环境中实现自动化冲突解决与共识构建。该系统结合深度强化学习架构与基于对话的谈判协议，使智能体可通过迭代沟通与策略适应进行复杂冲突处理。主要贡献包括：第一，提出一种分层共识网络（HCN）架构，融合注意力机制与图神经网络，以建模智能体间的依赖关系与冲突动态；第二，设计渐进式谈判协议（PNP），通过自适应让步策略组织多轮对话交互；第三，提出上下文感知奖励塑造机制，协调个体目标与集体共识目标。该框架显著提升了复杂情境下的协作效率与共识达成能力。
原文摘要 · Abstract (English)
Conflict resolution and consensus building represent critical challenges in multi-agent systems, negotiations, and collaborative decision-making processes. This paper introduces Dialogue Diplomats, a novel end-to-end multi-agent reinforcement learning (MARL) framework designed for automated conflict resolution and consensus building in complex, dynamic environments. The proposed system integrates advanced deep reinforcement learning architectures with dialogue-based negotiation protocols, enabling autonomous agents to engage in sophisticated conflict resolution through iterative communication and strategic adaptation. We present three primary contributions: first, a novel Hierarchical Consensus Network (HCN) architecture that combines attention mechanisms with graph neural networks to model inter-agent dependencies and conflict dynamics. second, a Progressive Negotiation Protocol (PNP) that structures multi-round dialogue interactions with adaptive concession strategies; and third, a Context-Aware Reward Shaping mechanism that balances individual agent objectives with collective consensus goals.
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