Tabletop Games framework

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), and we welcome collaborations!

Currently implemented games

  • Tic-Tac-Toe (c. 1850)
  • Dots and Boxes (Edouard Lucas 1889)
  • Love Letter (Seiji Kanai 2012)
  • Uno (Merle Robbins 1971)
  • Virus! (Cabrero and others 2015)
  • Exploding Kittens (Inman and others 2015)
  • Colt Express (Christophe Raimbault 2014)
  • Pandemic (Matt Leacock 2008)
  • Diamant (Bruno Faidutti and Alan R. Moon 2005)
  • Dominion (Donald X. Vaccarino 2008)
  • Poker Texas Hold’em (1810)
  • Blackjack (c. 1700)
  • Sushi Go (Phil Walker-Harding 2013)
  • BattleLore (Richard Borg and Robert Kouba 2013 - 2nd edition)
  • Stratego (Jacques Johan Mogendorff 1946)
  • Settlers of Catan (Klaus Teuber 1995)
  • Connect 4 (Ned Strongin and Howard Wexler 1974)
  • Can’t Stop (Sid Sackson 1980)

Games in progress:

  • Santorini (Lina Cossette, David Forest 2016)
  • Terraforming Mars (Jacob Fryxelius 2016)

Citing Information

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

		 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.}


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.