Designing an Automatic Agent for Repeated Language–based Persuasion Games

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 (decis...

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Bibliographic Details
Published in:Transactions of the Association for Computational Linguistics 2022-03, Vol.10, p.307-324
Main Authors: Raifer, Maya, Rotman, Guy, Apel, Reut, Tennenholtz, Moshe, Reichart, Roi
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
Games
Machine learning
Natural language processing
Persuasion
Search algorithms
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Summary: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 and its adaptability to different decision makers and potential proposed deals.
ISSN:2307-387X
2307-387X
DOI:10.1162/tacl_a_00462