A Dynamic Strategy Coach for Effective Negotiation

Zhou, Yiheng; He, He; Black, Alan W.; Tsvetkov, Yulia

doi:10.18653/v1/w19-5943

Cited by 18 publications

(12 citation statements)

References 19 publications

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“…In win-win negotiation, it is better to gain higher total negotiation utility. Two negotiation models are selected for comparison: 1) Coaching based negotiation model [1]; 2) Tritraining based negotiation model [6]. In the Random model, the agent didn't consider the opponent's utility in the process of generating a counter-proposal.…”

Section: Resultsmentioning

confidence: 99%

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Automated Negotiation Based on Ensemble Learning and Optimal Counter Proposal

Peng

Zheng

2020

Int. J. Eng. Tech. Mgmt. Res.

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Agent-mediated automated negotiation is a key form of interaction in the e-commerce environment. Agents reach an agreement through an iterative process of making offers. However, agents are prone to conceal their private negotiation information, which decreases the efficiency of negotiation. In this paper, an ensemble learning-based negotiation method is proposed. The new method labels the proposals automatically by mining the implicit information in negotiation history data. Then, the labeled proposals become the training samples of the ensemble learning algorithm, which generates the estimation of the opponent’s utility function. At last, based on the utility function of both sides, a win-win negotiation counter-proposal is generated through a particle swarm optimization algorithm. The experimental results indicate the benefits and efficiency of the proposed method.

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Section: Resultsmentioning

confidence: 99%

“…In an e-commerce environment, negotiation is a key form of interaction to reach an agreement [1][2][3][4]. With the rapid development of agent technology, an intelligent agent can decide for themselves what actions they might perform, at what time, and under what conditions.…”

Section: Introductionmentioning

confidence: 99%

Automated Negotiation Based on Ensemble Learning and Optimal Counter Proposal

Peng

Zheng

2020

Int. J. Eng. Tech. Mgmt. Res.

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“…AI-assisted communications or writing. Beyond paraphrasing, AI tools have been used to provide communication or writing assistance in diverse settings: from the mundane task of grammar and spell checking (Napoles et al, 2019;Stevens, 2019), to creative writing (Clark et al, 2018), to negotiations (Zhou et al, 2019), and has led to discussions of ethical implications (Hancock et al, 2020). Models of communication.…”

Section: Further Related Workmentioning

confidence: 99%

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Fussell

Danescu-Niculescu-Mizil

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker's intentions and the listener's perceptions in both cases.

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“…Developing dialog systems for negotiation is challenging since the task requires a combination of good communication skills and strategic reasoning capabilities (Traum et al, 2008;Young et al, 2013;Keizer et al, 2017). While recent neural models (Wen et al, 2017;Dhingra et al, 2017;Zhou et al, 2019;He et al, 2018) have shown that useful dialogue strategies can be learned from offline corpora, they do not explicitly model the mental state of other agents, which can make it challenging to generate tailored strategies and utterances for different types of opponents.…”

Section: Introductionmentioning

confidence: 99%

Improving Dialog Systems for Negotiation with Personality Modeling

Yang¹,

Chen²,

Narasimhan³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent's personality type during both learning and inference. We test our approach on the CRAIGSLISTBAR-GAIN dataset (He et al., 2018) and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents. We also find that our model displays diverse negotiation behavior with different types of opponents. 1

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A Dynamic Strategy Coach for Effective Negotiation

Abstract: Negotiation is a complex activity involving strategic reasoning, persuasion, and psychology.

Cited by 18 publications

References 19 publications

Automated Negotiation Based on Ensemble Learning and Optimal Counter Proposal

Automated Negotiation Based on Ensemble Learning and Optimal Counter Proposal

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Improving Dialog Systems for Negotiation with Personality Modeling

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