@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://preview.aclanthology.org/fix-sig-urls/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."
}