Designing an Automatic Agent for Repeated Language–based Persuasion Games

Maya Raifer, Guy Rotman, Reut Apel, Moshe Tennenholtz, Roi Reichart


Abstract
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.1
Anthology ID:
2022.tacl-1.18
Volume:
Transactions of the Association for Computational Linguistics, Volume 10
Month:
Year:
2022
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
307–324
Language:
URL:
https://aclanthology.org/2022.tacl-1.18
DOI:
10.1162/tacl_a_00462
Bibkey:
Cite (ACL):
Maya Raifer, Guy Rotman, Reut Apel, Moshe Tennenholtz, and Roi Reichart. 2022. Designing an Automatic Agent for Repeated Language–based Persuasion Games. Transactions of the Association for Computational Linguistics, 10:307–324.
Cite (Informal):
Designing an Automatic Agent for Repeated Language–based Persuasion Games (Raifer et al., TACL 2022)
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PDF:
https://aclanthology.org/2022.tacl-1.18.pdf