Design and Implementation of TAG: A Tabletop Games Framework
This framework addresses the need for standardized tools in Game AI research, though it is incremental as it builds on existing benchmarking concepts.
The authors introduced TAG, a Java-based framework for developing and benchmarking tabletop games for AI research, providing a common API, components for adding games, and logging for analysis, with seven games implemented as examples.
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