Welcome to the Tabletop Games framework!

The Tabletop Games Framework (TAG) is a Java-based benchmark for developing modern board games for AI research. TAG provides a common skeleton for implementing tabletop games based on a common API for AI agents, a set of components and classes to easily add new games and an import module for defining data in JSON format. At present, this platform includes the implementation of several tabletop games that can also be used as an example for further developments. Additionally, TAG also incorporates logging functionality that allows the user to perform a detailed analysis of the game, in terms of action space, branching factor, hidden information, and other measures of interest for Game AI research.

The code is all publicly available on GitHub (details on the Resources page). The framework is maintained by the Games AI Research Group at Queen Mary, University of London, and we welcome collaborations!

To get started, browse the wiki, or check out this introductory PDF.

• Join our community and Discord server HERE!

• Find out more about our spin-out company, Tabletop R&D HERE!

NEW!! PyTAG!

A Python interface for TAG. You can now write players for games in TAG from Python:

• Github repository: HERE
• Research paper: HERE

Currently implemented games

Games in progress:

• Ticket to Ride (Alan R. Moon, 2004)
• Descent: Journeys in the Dark, 2nd edition (Daniel Clark, Corey Konieczka, Adam Sadler and Kevin Wilson, 2013)
• Secret Hitler (Mike Boxleiter, Tommy Maranges, Max Temkin, 2016)

Citing Information

To cite TAG in your work, please cite this paper:

@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.}
	}

Acknowledgements

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.