Dialogue Diplomats: An End-to-End Multi-Agent Reinforcement Learning System for Automated Conflict Resolution and Consensus Building

TL;DR

Dialogue Diplomats tackles automated conflict resolution and consensus building in multi-agent systems by integrating an end-to-end MARL framework with structured dialogue. The approach models interactions as a MAPOMDP and optimizes a multi-objective objective $J = α ∑_{i=1}^{N} U_i(X^*) + β · Consensus(X^*) - γ · Time(X^*)$, enabling simultaneous consideration of individual utilities, collective agreement, and efficiency. The work introduces the Hierarchical Consensus Network (HCN), the Progressive Negotiation Protocol (PNP), and a Context-Aware Reward Shaping mechanism, trained with PPO in curriculum and population-diverse regimes, achieving strong performance across domains. Empirical results show high consensus rates (≈94%), faster convergence, improved social welfare and fairness, and scalability to dozens of agents, underscoring potential for scalable automated negotiation in domains from diplomacy to supply chain coordination.

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.