MIND: Multi-agent inference for negotiation dialogue in travel planning

TL;DR

MIND is a multi-agent negotiation framework for travel planning that leverages Theory of Mind to infer preferences, outperforming traditional methods in consensus-building and stakeholder satisfaction.

Contribution

Introduces MIND, a novel multi-agent negotiation framework using Theory of Mind to improve consensus accuracy and stakeholder satisfaction in complex travel planning scenarios.

Findings

Achieves 90.2% accuracy in inferring opponent willingness.

Improves High-w Hit by 20.5% and Debate Hit-Rate by 30.7%.

Surpasses baselines in Rationality (68.8%) and Fluency (72.4%).

Abstract

While Multi-Agent Debate (MAD) research has advanced, its efficacy in coordinating complex stakeholder interests such as travel planning remains largely unexplored. To bridge this gap, we propose MIND (Multi-agent Inference for Negotiation Dialogue), a framework designed to simulate realistic consensus-building among travelers with heterogeneous preferences. Grounded in the Theory of Mind (ToM), MIND introduces a Strategic Appraisal phase that infers opponent willingness (w) from linguistic nuances with 90.2% accuracy. Experimental results demonstrate that MIND outperforms traditional MAD frameworks, achieving a 20.5% improvement in High-w Hit and a 30.7% increase in Debate Hit-Rate, effectively prioritizing high-stakes constraints. Furthermore, qualitative evaluations via LLM-as-a-Judge confirm that MIND surpasses baselines in Rationality (68.8%) and Fluency (72.4%), securing an overall win rate of 68.3%. These findings validate that MIND effectively models human negotiation dynamics to derive persuasive consensus.