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

TL;DR

This paper presents Dialogue Diplomats, an end-to-end multi-agent reinforcement learning system that uses dialogue-based negotiation and advanced neural architectures to automate conflict resolution and consensus building in complex environments.

Contribution

It introduces a novel Hierarchical Consensus Network, a Progressive Negotiation Protocol, and a Context-Aware Reward Shaping mechanism for improved multi-agent conflict resolution.

Findings

Effective multi-round dialogue negotiation achieved

Enhanced conflict resolution through hierarchical modeling

Balanced individual and collective objectives

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.