AI-based playtesting of contemporary board games

Fernando De Mesentier Silva, Scott Lee, Julian Togelius, Andy Nealen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Ticket to Ride is a popular contemporary board game for two to four players, featuring a number of expansions with additional maps and tweaks to the core game mechanics. In this paper, four different game-playing agents that embody different playing styles are defined and used to analyze Ticket to Ride. Different playing styles are shown to be effective depending on the map and rule variation, and also depending on how many players play the game. The performance profiles of the different agents can be used to characterize maps and identify the most similar maps in the space of playstyles. Further analysis of the automatically played games reveal which cities on the map are most desirable, and that the relative attractiveness of cities is remarkably consistent across numbers of players. Finally, the automated analysis also reveals two classes of failures states, where the agents find states which are not covered by the game rules; this is akin to finding bugs in the rules. We see the analysis performed here as a possible template for AI-based playtesting of contemporary board games.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 12th International Conference on the Foundations of Digital Games, FDG 2017
    PublisherAssociation for Computing Machinery
    VolumePart F130151
    ISBN (Electronic)9781450353199
    DOIs
    StatePublished - Aug 14 2017
    Event12th International Conference on the Foundations of Digital Games, FDG 2017 - Cape Cod, United States
    Duration: Aug 14 2017Aug 17 2017

    Other

    Other12th International Conference on the Foundations of Digital Games, FDG 2017
    CountryUnited States
    CityCape Cod
    Period8/14/178/17/17

    Fingerprint

    Mechanics

    Keywords

    • Artificial Intelligence
    • Board Games
    • Contemporary Board Games
    • Playtesting
    • Ticket to Ride

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Computer Networks and Communications
    • Computer Vision and Pattern Recognition
    • Software

    Cite this

    Mesentier Silva, F. D., Lee, S., Togelius, J., & Nealen, A. (2017). AI-based playtesting of contemporary board games. In Proceedings of the 12th International Conference on the Foundations of Digital Games, FDG 2017 (Vol. Part F130151). [A13] Association for Computing Machinery. https://doi.org/10.1145/3102071.3102105

    AI-based playtesting of contemporary board games. / Mesentier Silva, Fernando De; Lee, Scott; Togelius, Julian; Nealen, Andy.

    Proceedings of the 12th International Conference on the Foundations of Digital Games, FDG 2017. Vol. Part F130151 Association for Computing Machinery, 2017. A13.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Mesentier Silva, FD, Lee, S, Togelius, J & Nealen, A 2017, AI-based playtesting of contemporary board games. in Proceedings of the 12th International Conference on the Foundations of Digital Games, FDG 2017. vol. Part F130151, A13, Association for Computing Machinery, 12th International Conference on the Foundations of Digital Games, FDG 2017, Cape Cod, United States, 8/14/17. https://doi.org/10.1145/3102071.3102105
    Mesentier Silva FD, Lee S, Togelius J, Nealen A. AI-based playtesting of contemporary board games. In Proceedings of the 12th International Conference on the Foundations of Digital Games, FDG 2017. Vol. Part F130151. Association for Computing Machinery. 2017. A13 https://doi.org/10.1145/3102071.3102105
    Mesentier Silva, Fernando De ; Lee, Scott ; Togelius, Julian ; Nealen, Andy. / AI-based playtesting of contemporary board games. Proceedings of the 12th International Conference on the Foundations of Digital Games, FDG 2017. Vol. Part F130151 Association for Computing Machinery, 2017.
    @inproceedings{6c4319a67acf47d191469bed2d3c4d0f,
    title = "AI-based playtesting of contemporary board games",
    abstract = "Ticket to Ride is a popular contemporary board game for two to four players, featuring a number of expansions with additional maps and tweaks to the core game mechanics. In this paper, four different game-playing agents that embody different playing styles are defined and used to analyze Ticket to Ride. Different playing styles are shown to be effective depending on the map and rule variation, and also depending on how many players play the game. The performance profiles of the different agents can be used to characterize maps and identify the most similar maps in the space of playstyles. Further analysis of the automatically played games reveal which cities on the map are most desirable, and that the relative attractiveness of cities is remarkably consistent across numbers of players. Finally, the automated analysis also reveals two classes of failures states, where the agents find states which are not covered by the game rules; this is akin to finding bugs in the rules. We see the analysis performed here as a possible template for AI-based playtesting of contemporary board games.",
    keywords = "Artificial Intelligence, Board Games, Contemporary Board Games, Playtesting, Ticket to Ride",
    author = "{Mesentier Silva}, {Fernando De} and Scott Lee and Julian Togelius and Andy Nealen",
    year = "2017",
    month = "8",
    day = "14",
    doi = "10.1145/3102071.3102105",
    language = "English (US)",
    volume = "Part F130151",
    booktitle = "Proceedings of the 12th International Conference on the Foundations of Digital Games, FDG 2017",
    publisher = "Association for Computing Machinery",

    }

    TY - GEN

    T1 - AI-based playtesting of contemporary board games

    AU - Mesentier Silva, Fernando De

    AU - Lee, Scott

    AU - Togelius, Julian

    AU - Nealen, Andy

    PY - 2017/8/14

    Y1 - 2017/8/14

    N2 - Ticket to Ride is a popular contemporary board game for two to four players, featuring a number of expansions with additional maps and tweaks to the core game mechanics. In this paper, four different game-playing agents that embody different playing styles are defined and used to analyze Ticket to Ride. Different playing styles are shown to be effective depending on the map and rule variation, and also depending on how many players play the game. The performance profiles of the different agents can be used to characterize maps and identify the most similar maps in the space of playstyles. Further analysis of the automatically played games reveal which cities on the map are most desirable, and that the relative attractiveness of cities is remarkably consistent across numbers of players. Finally, the automated analysis also reveals two classes of failures states, where the agents find states which are not covered by the game rules; this is akin to finding bugs in the rules. We see the analysis performed here as a possible template for AI-based playtesting of contemporary board games.

    AB - Ticket to Ride is a popular contemporary board game for two to four players, featuring a number of expansions with additional maps and tweaks to the core game mechanics. In this paper, four different game-playing agents that embody different playing styles are defined and used to analyze Ticket to Ride. Different playing styles are shown to be effective depending on the map and rule variation, and also depending on how many players play the game. The performance profiles of the different agents can be used to characterize maps and identify the most similar maps in the space of playstyles. Further analysis of the automatically played games reveal which cities on the map are most desirable, and that the relative attractiveness of cities is remarkably consistent across numbers of players. Finally, the automated analysis also reveals two classes of failures states, where the agents find states which are not covered by the game rules; this is akin to finding bugs in the rules. We see the analysis performed here as a possible template for AI-based playtesting of contemporary board games.

    KW - Artificial Intelligence

    KW - Board Games

    KW - Contemporary Board Games

    KW - Playtesting

    KW - Ticket to Ride

    UR - http://www.scopus.com/inward/record.url?scp=85030788695&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85030788695&partnerID=8YFLogxK

    U2 - 10.1145/3102071.3102105

    DO - 10.1145/3102071.3102105

    M3 - Conference contribution

    VL - Part F130151

    BT - Proceedings of the 12th International Conference on the Foundations of Digital Games, FDG 2017

    PB - Association for Computing Machinery

    ER -