A Dual-Mind Framework for Strategic and Expressive Negotiation Agent

Abstract

Negotiation agents need to influence the attitudes or intentions of users to reach a consensus. Strategy planning and expressive optimization are crucial aspects of effective negotiations. However, previous studies have typically focused on only one of these aspects, neglecting the fact that their combined synergistic effect can lead to better performance. Inspired by the dual-process theory in human cognition, we propose a Dual-Mind Negotiation Agent (DMNA) framework. This framework integrates an intuitive module for rapid, experience-based response and a deliberative module for slow, expression optimization. The intuitive module is trained using Monte Carlo Tree Search (MCTS) and Direct Preference Optimization (DPO), enabling it to make suitable strategic planning and expression. The deliberative module employs a multifaceted reflexion mechanism to enhance the quality of expression. Experiments conducted on negotiation datasets confirm that DMNA achieves state-of-the-art results, demonstrating an enhancement in the negotiation ability of agents.