Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | AIIDE 2020 Workshops |
| Untertitel | Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020) |
| Publikationsstatus | Veröffentlicht - 2020 |
| Extern publiziert | Ja |
| Veranstaltung | 2020 Joint Artificial Intelligence and Interactive Digital Entertainment Workshops, AIIDE-WS 2020 - Worcester, USA / Vereinigte Staaten Dauer: 19 Okt. 2020 → … |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 2862 |
| ISSN (Print) | 1613-0073 |
Abstract
Tabletop games come in a variety of forms, including board games, card games, and dice games. In recent years, their complexity has considerably increased, with many components, rules that change dynamically through the game, diverse player roles, and a series of control parameters that influence a game's balance. As such, they also encompass novel and intricate challenges for Artificial Intelligence methods, yet research largely focuses on classical board games such as chess and Go. We introduce in this work the Tabletop Games (TAG) framework, which promotes research into general AI in modern tabletop games, facilitating the implementation of new games and AI players, while providing analytics to capture the complexities of the challenges proposed. We include preliminary results with sample AI players, showing some moderate success, with plenty of room for improvement, and discuss further developments and new research directions.
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AIIDE 2020 Workshops: Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020). 2020. (CEUR Workshop Proceedings; Band 2862).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - TAG
T2 - 2020 Joint Artificial Intelligence and Interactive Digital Entertainment Workshops, AIIDE-WS 2020
AU - Gaina, Raluca D.
AU - Balla, Martin
AU - Dockhorn, Alexander
AU - Montoliu, Raúl
AU - Perez-Liebana, Diego
N1 - Funding Information: This work was partly funded by the EPSRC CDT in Intelligent Games and Game Intelligence (IGGI) EP/L015846/1 and EPSRC research grant EP/T008962/1.
PY - 2020
Y1 - 2020
N2 - Tabletop games come in a variety of forms, including board games, card games, and dice games. In recent years, their complexity has considerably increased, with many components, rules that change dynamically through the game, diverse player roles, and a series of control parameters that influence a game's balance. As such, they also encompass novel and intricate challenges for Artificial Intelligence methods, yet research largely focuses on classical board games such as chess and Go. We introduce in this work the Tabletop Games (TAG) framework, which promotes research into general AI in modern tabletop games, facilitating the implementation of new games and AI players, while providing analytics to capture the complexities of the challenges proposed. We include preliminary results with sample AI players, showing some moderate success, with plenty of room for improvement, and discuss further developments and new research directions.
AB - Tabletop games come in a variety of forms, including board games, card games, and dice games. In recent years, their complexity has considerably increased, with many components, rules that change dynamically through the game, diverse player roles, and a series of control parameters that influence a game's balance. As such, they also encompass novel and intricate challenges for Artificial Intelligence methods, yet research largely focuses on classical board games such as chess and Go. We introduce in this work the Tabletop Games (TAG) framework, which promotes research into general AI in modern tabletop games, facilitating the implementation of new games and AI players, while providing analytics to capture the complexities of the challenges proposed. We include preliminary results with sample AI players, showing some moderate success, with plenty of room for improvement, and discuss further developments and new research directions.
UR - http://www.scopus.com/inward/record.url?scp=85106171753&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2009.12065
DO - 10.48550/arXiv.2009.12065
M3 - Conference contribution
AN - SCOPUS:85106171753
T3 - CEUR Workshop Proceedings
BT - AIIDE 2020 Workshops
Y2 - 19 October 2020
ER -