GAIG Game AI Research Group @ QMUL

Design and Implemenation of TAG: A Tabletop Games Framework


Abstract

This document describes the design and implementation of the Tabletop Games framework (TAG), 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 seven different 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 objective of this document is to serve as a central point where the framework can be described at length. TAG can be downloaded at: https://github.com/GAIGResearch/TabletopGames
Arxiv: https://arxiv.org/abs/2009.12065 
Github: https://github.com/GAIGResearch/TabletopGames 

Cite this work

@article{gaina2020tagdesign,
author= {Raluca D. Gaina and Martin Balla and Alexander Dockhorn and Raul Montoliu and Diego Perez-Liebana},
title= {{Design and Implemenation of TAG: A Tabletop Games Framework}},
year= {2020},
journal= {{arxiv:2009.12065}},
abstract= {This document describes the design and implementation of the Tabletop Games framework (TAG), 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 seven different 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 objective of this document is to serve as a central point where the framework can be described at length. TAG can be downloaded at: https://github.com/GAIGResearch/TabletopGames},
}

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