@inproceedings{gu-etal-2025-personalized,
title = "Personalized Help for Optimizing Low-Skilled Users' Strategy",
author = "Gu, Feng and
Wongkamjan, Wichayaporn and
Boyd-Graber, Jordan Lee and
Kummerfeld, Jonathan K. and
Peskoff, Denis and
May, Jonathan",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "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)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-short.6/",
pages = "65--74",
ISBN = "979-8-89176-190-2",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Personalized Help for Optimizing Low-Skilled Users’ Strategy
%A Gu, Feng
%A Wongkamjan, Wichayaporn
%A Boyd-Graber, Jordan Lee
%A Kummerfeld, Jonathan K.
%A Peskoff, Denis
%A May, Jonathan
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-190-2
%F gu-etal-2025-personalized
%X 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.
%U https://aclanthology.org/2025.naacl-short.6/
%P 65-74
Markdown (Informal)
[Personalized Help for Optimizing Low-Skilled Users’ Strategy](https://aclanthology.org/2025.naacl-short.6/) (Gu et al., NAACL 2025)
ACL
- Feng Gu, Wichayaporn Wongkamjan, Jordan Lee Boyd-Graber, Jonathan K. Kummerfeld, Denis Peskoff, and Jonathan May. 2025. Personalized Help for Optimizing Low-Skilled Users’ Strategy. In 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), pages 65–74, Albuquerque, New Mexico. Association for Computational Linguistics.