Wichayaporn Wongkamjan
2025
Personalized Help for Optimizing Low-Skilled Users’ Strategy
Feng Gu
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Wichayaporn Wongkamjan
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Jordan Lee Boyd-Graber
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Jonathan K. Kummerfeld
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Denis Peskoff
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Jonathan May
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
AIs can beat humans in game environments; however, how helpful those agents are to human remains understudied. We augment Cicero, a natural language agent that demonstrates superhuman performance in Diplomacy, to generate both move and message advice based on player intentions. A dozen Diplomacy games with novice and experienced players, with varying advice settings, show that some of the generated advice is beneficial. It helps novices compete with experienced players and in some instances even surpass them. The mere presence of advice can be advantageous, even if players do not follow it.
2024
More Victories, Less Cooperation: Assessing Cicero’s Diplomacy Play
Wichayaporn Wongkamjan
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Feng Gu
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Yanze Wang
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Ulf Hermjakob
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Jonathan May
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Brandon M. Stewart
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Jonathan K. Kummerfeld
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Denis Peskoff
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Jordan Lee Boyd-Graber
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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.
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Co-authors
- Jordan Lee Boyd-Graber 2
- Feng Gu 2
- Jonathan K. Kummerfeld 2
- Jonathan May 2
- Denis Peskoff 2
- show all...