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 dialog. Our negotiation coach monitors messages between them and recommends tactics 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 and tactics largely depend on the context (e.g., the current price, the buyer's attitude). Therefore, we first identify a set of negotiation tactics, then learn to predict the best strategy and tactics in a given dialog context from a set of human-human bargaining dialogs. Evaluation on human-human dialogs shows that our coach increases the profits of the seller by almost 60%.

利用“机器-循环”方法，帮助人们在文本对话中进行更好的谈判，从而获得更好的利润。通过学习人与人之间的谈判对话，在特定情境下预测最佳策略和战术，推荐给卖家实时应对，从而达到更好的成交效果。

有效谈判的动态策略教练