More Victories, Less Cooperation: Assessing Cicero’s Diplomacy Play
Wichayaporn Wongkamjan, Feng Gu, Yanze Wang, Ulf Hermjakob, Jonathan May, Brandon M. Stewart, Jonathan K. Kummerfeld, Denis Peskoff
Abstract
The boardgame Diplomacy is a challenging setting for communicative and cooperative artificial intelligence. The most prominent communicative Diplomacy AI, Cicero, has excellent strategic abilities, exceeding human players. However, the best Diplomacy players master communication, not just tactics, which is why the game has received attention as an AI challenge. This work seeks to understand the degree to which Cicero succeeds at communication. First, we annotate in-game communication with abstract meaning representation to separate in-game tactics from general language. Second, we run two dozen games with humans and Cicero, totaling over 200 human-player hours of competition. While AI can consistently outplay human players, AI-Human communication is still limited because of AI’s difficulty with deception and persuasion. This shows that Cicero relies on strategy and has not yet reached the full promise of communicative and cooperative AI.
BibTeX
@inproceedings{wongkamjan-etal-2024-victories,
title = "More Victories, Less Cooperation: Assessing Cicero`s Diplomacy Play",
author = "Wongkamjan, Wichayaporn and
Gu, Feng and
Wang, Yanze and
Hermjakob, Ulf and
May, Jonathan and
Stewart, Brandon M. and
Kummerfeld, Jonathan K. and
Peskoff, Denis and
Boyd-Graber, Jordan Lee",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.672/",
doi = "10.18653/v1/2024.acl-long.672",
pages = "12423--12441"
}