A Dynamic Strategy Coach for Effective Negotiation
A Dynamic Strategy Coach for Effective Negotiation
复制标题
有效谈判的动态策略教练
DOI：
10.18653/v1/w19-5943
复制
发表时间：
2019
影响因子：
3.5
通讯作者：
Yulia Tsvetkov
中科院分区：
文献类型：
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作者：
Yiheng Zhou;He He;A. Black;Yulia Tsvetkov
谈判是一项复杂的活动，涉及战略推理，说服和心理学。一个普通人往往远非谈判专家。我们的目标是通过机器在环方法帮助人类成为更好的谈判者，该方法结合了机器在数据驱动决策方面的优势和人类的语言生成能力。我们考虑一个讨价还价的情况下，卖方和买方通过基于文本的对话谈判的价格为销售的项目。我们的谈判教练监控他们之间的消息，并在真实的时间向卖方推荐策略，以获得更好的交易（例如，“拒绝提案并提出一个价格”，“谈谈你对产品的个人体验”）。最佳策略在很大程度上取决于上下文（例如，当前价格，买家的态度）。因此，我们首先确定一组谈判策略，然后学习从一组人与人的谈判对话中预测给定对话背景下的最佳策略。对人与人之间对话的评估表明，我们的教练使卖方的利润增加了近60%。
Negotiation is a complex activity involving strategic reasoning, persuasion, and psychology. An average person is often far from an expert in negotiation. Our goal is to assist humans to become better negotiators through a machine-in-the-loop approach that combines machine’s advantage at data-driven decision-making and human’s language generation ability. We consider a bargaining scenario where a seller and a buyer negotiate the price of an item for sale through a text-based dialogue. Our negotiation coach monitors messages between them and recommends strategies in real time to the seller to get a better deal (e.g., “reject the proposal and propose a price”, “talk about your personal experience with the product”). The best strategy largely depends on the context (e.g., the current price, the buyer’s attitude). Therefore, we first identify a set of negotiation strategies, then learn to predict the best strategy in a given dialogue context from a set of human-human bargaining dialogues. Evaluation on human-human dialogues shows that our coach increases the profits of the seller by almost 60%.
影响因子：
3.6
作者：
Nasi, Ravindranath;Patrick, Brian O.;Pinto, B. Mario
通讯作者：
Pinto, B. Mario