INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue Agent (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a novel negotiation agent is designed for the online marketplace . a dialogue agent can negotiate on price and other factors . |
| Approach: | They propose a novel negotiation agent that is integrative in nature and can negotiate on price and other factors. |
| Outcome: | The proposed agent is integrative in nature and can negotiate on price and other factors. |
Similar Papers
TRIP NEGOTIATOR: A Travel Persona-aware Reinforced Dialogue Generation Model for Personalized Integrative Negotiation in Tourism (2024.findings-emnlp)
Copied to clipboard
Priyanshu Priya, Desai Yasheshbhai, Ratnesh Joshi, Roshni Ramnani, Anutosh Maitra, Shubhashis Sengupta, Asif Ekbal
| Challenge: | Incorporating traveler preferences, constraints, and expectations allows for customizing negotiation strategies, resulting in a more personalized and integrative experience. |
| Approach: | They propose a novel travel persona-aware Reinforced dIalogue generation model for personalized integrative negotiation in the tourism domain. |
| Outcome: | The proposed system generates coherent and diverse responses consistent with the traveler's personality. |
Decoupling Strategy and Generation in Negotiation Dialogues (D18-1)
Copied to clipboard
| Challenge: | Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy. |
| Approach: | They propose a modular approach that decouples strategy and generation by coarse dialogue acts . they test their approach on a recently proposed DEALORNODEAL game . |
| Outcome: | The proposed approach can decouple strategy and generation without degeneracy. |
ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimization (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. |
| Approach: | They propose a framework for turn-level offer optimization based on two core principles: opponent modeling and Tit-for-Tat reciprocity. |
| Outcome: | The proposed framework outperforms baselines across diverse partner agents and validates through human evaluation. |
Let’s Negotiate! A Survey of Negotiation Dialogue Systems (2024.findings-eacl)
Copied to clipboard
Haolan Zhan, Yufei Wang, Zhuang Li, Tao Feng, Yuncheng Hua, Suraj Sharma, Lizhen Qu, Zhaleh Semnani Azad, Ingrid Zukerman, Reza Haf
| Challenge: | Recent research has focused on negotiation dialogue systems, but no systematic review of this task has been conducted. |
| Approach: | They propose to provide a systematic review of negotiation dialogue systems and to provide an overview of current research. |
| Outcome: | The proposed systems are based on the literature and are compared against existing systems. |
Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that virtual agents can help humans achieve task and social goals. |
| Approach: | They propose a tuning-free and label-free method to identify high-quality ICL exemplars for the remediator agent and propose measurable criteria to measure the quality of the negotiation outcomes. |
| Outcome: | The proposed model is able to improve negotiation outcomes across three negotiation topics. |
Leveraging Implicit Feedback from Deployment Data in Dialogue (2024.eacl-short)
Copied to clipboard
| Challenge: | Xu et al., 2023) and Bai ed., 2019) use crowdworkers to collect signals from natural dialogue episodes. |
| Approach: | They use the publicly released BlenderBot deployment data to extract signals from conversations to implicitly measure the quality of a machine-generated utterance. |
| Outcome: | The proposed model improves over baseline models, but some proxy signals can lead to undesirable generations. |
Enhancing Persuasive Dialogue Agents by Synthesizing Cross‐Disciplinary Communication Strategies (2025.emnlp-industry)
Copied to clipboard
Shinnosuke Nozue, Yuto Nakano, Yotaro Watanabe, Meguru Takasaki, Shoji Moriya, Reina Akama, Jun Suzuki
| Challenge: | Current approaches to developing persuasive dialogue agents rely on predefined persuasive strategies that fail to capture the complexity of real-world interactions. |
| Approach: | They propose a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory. |
| Outcome: | The proposed framework demonstrated significant improvement in the persuasion success rate and generalizability of the datasets. |
Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method (2024.findings-acl)
Copied to clipboard
| Challenge: | Using a novel approach, we can evaluate an agent’s bargaining abilities as an asymmetric incomplete information game. |
| Approach: | They propose an approach that integrates a deterministic Offer Generator and an LLM Narrator to create natural language sentences for generated offers. |
| Outcome: | The proposed approach improves the buyer’s deal rates from 26.67% to 88.88% and brings a ten times multiplication of profits on all baselines, even a model that has not been aligned. |
A Fairness-Driven Method for Learning Human-Compatible Negotiation Strategies (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in AI and NLP have led researchers to develop techniques to build autonomous agents which can achieve human-level performance in bargaining games such as Deal-orno-Deal. |
| Approach: | They propose a negotiation framework which incorporates fairness into reward design and search to learn human-compatible negotiation strategies. |
| Outcome: | The proposed framework achieves more egalitarian negotiation outcomes and improves negotiation quality. |
A Dual-Mind Framework for Strategic and Expressive Negotiation Agent (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to negotiation dialogue focus on only one aspect, ignoring the synergistic effect of their combined synergies. |
| Approach: | They propose a dual-mind negotiation agent framework that integrates an intuitive and a deliberative module for slow, expression optimization. |
| Outcome: | The proposed framework achieves state-of-the-art on negotiation datasets showing that it improves negotiation ability. |