GAIG Game AI Research Group @ QMUL

TAG: A Tabletop Games Framework


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.
Github: https://github.com/GAIGResearch/TabletopGames 

Cite this work

@inproceedings{gaina2020tag,
author= {Raluca D. Gaina and Martin Balla and Alexander Dockhorn and Raul Montoliu and Diego Perez-Liebana},
title= {{TAG: A Tabletop Games Framework}},
year= {2020},
booktitle= {{Experimental AI in Games (EXAG), AIIDE 2020 Workshop}},
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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