대화 외교관 다중 에이전트 강화학습 기반 갈등 해결 및 합의 형성 프레임워크

📝 Abstract

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. Extensive experiments across diverse scenarios including resource allocation, multi-party negotiations, and crisis management simulations demonstrate that Dialogue Diplomats achieves superior performance compared to existing approaches, with average consensus rates exceeding 94.2% and conflict resolution times reduced by 37.8%. The system exhibits robust generalization capabilities across varied negotiation contexts and scales effectively to accommodate up to 50 concurrent negotiating agents. This work advances the state-of-the-art in automated negotiation systems and establishes foundational methodologies for deploying AI-driven consensus-building solutions in real-world applications spanning international diplomacy, organizational management, autonomous vehicle coordination, and distributed computing systems.

💡 Analysis

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. Extensive experiments across diverse scenarios including resource allocation, multi-party negotiations, and crisis management simulations demonstrate that Dialogue Diplomats achieves superior performance compared to existing approaches, with average consensus rates exceeding 94.2% and conflict resolution times reduced by 37.8%. The system exhibits robust generalization capabilities across varied negotiation contexts and scales effectively to accommodate up to 50 concurrent negotiating agents. This work advances the state-of-the-art in automated negotiation systems and establishes foundational methodologies for deploying AI-driven consensus-building solutions in real-world applications spanning international diplomacy, organizational management, autonomous vehicle coordination, and distributed computing systems.

📄 Content

Conflict resolution and consensus building constitute fundamental challenges across diverse domains, including international diplomacy, organizational management, supply chain coordination, autonomous vehicle navigation, and distributed computing systems. Traditional approaches to these challenges rely heavily on human negotiators, rule-based systems, or centralized coordination mechanisms, which often prove inadequate in handling the complexity, dynamism, and scale characteristic of modern multi-agent environments. The emergence of multi-agent reinforcement learning (MARL) offers promising avenues for developing automated systems capable of learning sophisticated negotiation strategies through environmental interaction and experience accumulation. However, despite significant progress in recent years, existing methodologies continue to face substantial limitations when applied to realistic conflict resolution scenarios.

Despite significant advances in MARL research over the past decade, existing approaches face critical limitations when applied to conflict resolution and consensus building scenarios. First, conventional MARL algorithms typically optimize individual agent rewards without explicit mechanisms for facilitating collective agreement or resolving conflicting objectives. Second, most existing systems lack structured communication protocols that enable agents to engage in meaningful dialogue, articulate preferences, propose compromises, and iteratively refine solutions through multi-round interactions. Third, current methodologies struggle with scalability challenges when the number of negotiating agents increases beyond smallscale scenarios, often experiencing exponential growth in computational complexity and training instability. Fourth, existing approaches often fail to generalize across diverse negotiation contexts, requiring extensive retraining for different conflict resolution domains and exhibiting brittleness when confronting novel scenarios.

This paper introduces Dialogue Diplomats, a comprehensive end-to-end MARL system specifically designed to address these limitations and advance automated conflict resolution and consensus building capabilities. The proposed framework synthesizes multiple research streams including deep reinforcement learning, graph neural networks, attention mechanisms, dialogue systems, and game-theoretic negotiation protocols into a unified architecture optimized for complex multi-party negotiations. Unlike previous approaches that adapt general-purpose MARL algorithms to negotiation settings, Dialogue Diplomats incorporates negotiation-specific mechanisms throughout the architecture, from perception and representation learning to strategic reasoning and action execution. This holistic design philosophy enables more effective learning and superior performance across diverse conflict resolution scenarios.

The motivation for developing Dialogue Diplomats stems from several converging technological and societal trends that collectively highlight the urgent need for automated conflict resolution capabilities. First, the proliferation of autonomous systems across transportation, robotics, and cyber-physical domains necessitates robust conflict resolution mechanisms that operate without continuous human oversight. Self-driving vehicles must coordinate at intersections, autonomous drones must share airspace, and robotic systems must allocate shared resources, all requiring real-time conflict resolution. Second, the increasing complexity of organizational decision-making processes demands scalable consensus-building tools that can synthesize diverse stakeholder perspectives efficiently. Modern organizations face decisions involving numerous stakeholders with heterogeneous preferences, limited time for deliberation, and high costs of failed coordination. Third, the growing interest in AI-augmented negotiation platforms for commercial applications highlights the practical value of automated negotiation systems. Electronic commerce, supply chain management, and business-to-business contracting increasingly involve automated agents negotiating terms on behalf of human principals.

We formulate the automated conflict resolution problem as a multi-agent partially observable Markov decision process (MAPOMDP) where N agents must reach consensus on a set of decision variables X = {x 1 , x 2 , . . . , x M } through structured dialogue interactions. Each agent i ∈ {1, . . . , N } maintains private preferences represented by utility function U i (X) : R M → R, observes partial environment state s t i ∈ S i at time t, and selects actions a t i ∈ A i from a hybrid action space comprising discrete negotiation moves such as propose, accept, reject, and counteroffer, along with continuous parameter adjustments specifying proposal values and concession magnitudes.

The system objective combines individual agent utilities with collective consensus metrics, formalized as:

where X * repres

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