Persuasion games are fundamental in economics and AI research and serve as the basis for important applications. However, work on this setup assumes communication with stylized messages that do not consist of rich human language. In this paper we consider a repeated sender (expert) -- receiver (decision maker) game, where the sender is fully informed about the state of the world and aims to persuade the receiver to accept a deal by sending one of several possible natural language reviews. We design an automatic expert that plays this repeated game, aiming to achieve the maximal payoff. Our expert is implemented within the Monte Carlo Tree Search (MCTS) algorithm, with deep learning models that exploit behavioral and linguistic signals in order to predict the next action of the decision maker, and the future payoff of the expert given the state of the game and a candidate review. We demonstrate the superiority of our expert over strong baselines, its adaptability to different decision makers, and that its selected reviews are nicely adapted to the proposed deal.

本文介绍了使用自然语言评论的重复发送者（专家）-接收者（决策者）游戏，利用MCTS算法和深度学习模型，以行为和语言信号为基础来预测下一个动作和未来的回报，进而形成口头说服。该专家相对于其他基准算法具有显著优势，并适用于不同的决策者。所选的评论适应于所提出的交易。

设计一个自动化的代理程序用于重复语言类说服游戏