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A Tabletop Games Framework

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A Tabletop Games Framework TAG: A Tabletop Games Framework Raluca D. Gaina*, Martin Balla*, Alexander Dockhorn*, Raul´ Montoliuy, Diego Perez-Liebana* *School of Electronic Engineering and Computer Science, Queen Mary University of London, UK yInsitute of New Imaging Technologies, Jaume I University, Castellon, Spain Abstract In this work, we introduce the Tabletop Games (TAG) Tabletop games come in a variety of forms, including board framework, which is a collection of tabletop games, agents, games, card games, and dice games. In recent years, their and development tools meant to provide a common platform complexity has considerably increased, with many compo- for AI research. This work is mainly motivated by three fac- nents, rules that change dynamically through the game, diverse tors: i) the characteristics of modern tabletop games (multi- player roles, and a series of control parameters that influence player, partially observable, large action spaces, competition, a game’s balance. As such, they also encompass novel and and collaboration, etc.) provide an interesting challenge to intricate challenges for Artificial Intelligence methods, yet re- AI research. These modern games provide many characteris- search largely focuses on classical board games such as chess tics not implemented in existing frameworks, e.g. changing and Go. We introduce in this work the Tabletop Games (TAG) player roles or varying forms of cooperative and competitive framework, which promotes research into general AI in mod- game-play; ii) TAG presents tabletop games from a GGP per- ern tabletop games, facilitating the implementation of new games and AI players, while providing analytics to capture the spective, by providing a common API for games and playing complexities of the challenges proposed. We include prelimi- agents; and iii) we aim to provide a framework that can in- nary results with sample AI players, showing some moderate corporate different games under a common platform, making success, with plenty of room for improvement, and discuss it possible for the research community to implement their further developments and new research directions. own games and AI players to expand TAG’s collection. De Araujo et al. (2019) highlighted the need for such a frame- 1 Introduction work in a survey that described the different schemes and data structures used in the literature of digital board games. In the last few decades, tabletop games have gone through a In order to provide the necessary flexibility to support the ‘Renaissance‘, gaining more popularity than ever: thousands great variety of existing tabletop games, TAG requires the of them are published each year and welcomed by an ex- user to implement games via a programming language (Java), panding audience of gamers (Engelstein and Shalev 2019). instead of using a game description language as other general Many of these games are typically designed with richer me- frameworks do. TAG provides many customisable compo- chanics and rules, and less focus on chance-based elements. nents to simplify the development of additional games not Modern tabletop games are very diverse and complex in gen- currently in the framework. It is able to handle partial observ- eral, which can provide various challenges, such as unique ability, providing simple means of adding custom observation game state representations, partial observability on various schemes, game-state heuristics, and agent statistics, as well levels, actions outside of a player’s turn, cooperation, and as supporting the development of graphical user interfaces competition in the same game, etc. Therefore, they have a for human play with computer players. very different and complex set of mechanisms that would While we are working on extending the framework with require a lot of effort to develop using previous approaches. additional game mechanics, games and agents, the current AI Research in board games has mostly been, with some version is publicly available1. The contributions of this paper exceptions, focused on traditional board games, either in are two-fold: firstly, we present the framework, its structure, isolation (chess, Othello, Go, etc.) or as part of general games and AI players implemented (see Section 3). Secondly, game playing (GGP) frameworks, such as GGP (Genesereth, we provide a discussion on a baseline experimentation (Sec- Love, and Pell 2005), Ludii (Piette et al. 2019) and Open- tion 4), aimed at illustrating insights into the games imple- Spiel (Lanctot and others 2019). While these frameworks also mented, their features, and performance of vanilla AI players. allow the definition of additional games, they are limited to In Section 5 we discuss challenges and opportunities included common mechanics or require extensive development effort. for this framework and we conclude with final remarks on Copyright c 2020 for this paper by its authors. Use permitted under future developments in Section 6. Creative Commons License Attribution 4.0 International (CC BY 4.0). 1https://github.com/GAIGResearch/TabletopGames 2 Related Work Spronck 2009) and Risk (Gibson, Desai, and Zhao 2010), AI research and board games have been closely related since and Rolling Horizon Evolutionary Algorithms (RHEA) in the beginnings of the field, with game-playing agents for Tic- Splendor (Bravi et al. 2019), all showing a great improve- Tac-Toe, Checkers and chess (Campbell and others 2002), and ment in performance. Other games have been more recently the more recent breakthroughs in Go (Silver et al. 2017). One highlighted as important challenges for AI players due to of the most well-known contests, the General Game Playing their strategic complexity (Pandemic (Chacon´ and Eger 2019; (GGP) competition (Genesereth, Love, and Pell 2005), fea- Sfikas and Liapis 2020)) or very large action spaces (Blood tured classical board games written in the Game Description bowl (Justesen et al. 2019)). Language and promoted research into generic game-players. Research has not only focused on game-playing AI, how- (Piette et al. 2019) later introduced the “ludemic” general ever. Ticket to Ride (de Mesentier Silva et al. 2017) was game system Ludii, which builds upon ideas from GGP. Ludii used as an example for employing AI players for play-testing defines games as structures of ludemes, high-level, easily un- games, characterising their features based on different play- derstandable game concepts, which allows for concise and styles and finding possible bugs or gaps in the ruleset. Further, human-understandable game descriptions, as well as easy the use of Procedural Content Generation for such games is implementation of new games. The current main focus of highlighted by (Guzdial et al. 2020); given the rule com- the Ludii project is on classical and ancient board games. plexities and the multitude of components in modern table- Kowalski et al. presented in (Kowalski et al. 2019) a new top games, AI methods can provide a more efficient way of GGP language, called Regular Boardgames (RBG), with a searching the possibility space for interesting variations. similar focus as Ludii, but which describes games as regular The Tabletop Games (TAG) framework introduced in this expressions instead, for increased efficiency. paper brings together all of these different research directions We consider direct code implementations to be more ac- and provides a common ground for the use of AI algorithms cessible, faster to execute and easier to work with in many in a variety of tabletop games, removing the effort of creating cases. More similarly to our framework in this regard, Open- different frameworks for different purposes and simplifying Spiel (Lanctot and others 2019) provides a collection of the overall development process. As far as we know, TAG is games and algorithms written in C++ and exposed to Python. the first framework that allows the development of multiple Most of their games are still traditional board games, with games and AI players under a common API for complex some exceptions, such as the inclusion of Hanabi. Differ- modern tabletop games. ently, TAG shifts the development effort onto the framework, rather than the games, by making a wide range of compo- 3 The Framework nents, rules, actions, etc. available to users. Our system allows TAG was designed to capture most of the complexity that for fast prototyping of new games and immediate extraction modern tabletop games provide, with a few games imple- of insights and features of interest in each game through mented already and more in progress. readily-available game and AI-facilitated analysis. We fur- ther support many research directions, from simulation-based 3.1 Concepts game-playing to parameter optimisation of games and artifi- cial players. Our framework includes handy definitions for various Here, we focus on more types of games, including board, concepts and components common across many tabletop card, dice and role-playing games, among others, often games (Engelstein and Shalev 2019). grouped under the tabletop games umbrella term. Tabletop We define an action as an independent unit of game logic Simulator (Henry 2015) is an example of software facili- that modifies a given game state towards a specific effect (e.g. tating implementation of tabletop games components in a player draws a card; player moves their pawn). These actions physics-based simulated environment; this allows players are executed by the game players and are subject to certain to interact with the games as they would in the real world, rules: units of game logic, part of a hierarchical structure (a but the many games implemented lack support for automatic game flow graph). Rules dictate how a given game state is rule execution, nor does the software facilitate AI research modified and control the flow through the game graph (for as targeted with TAG. However, tabletop games research has instance, checking the end of game conditions and the turn been gaining popularity in recent years. Research in game- order). This turn order defines which player is due to play playing agents for card games is common in competitive at each time, possibly handling player reactions forced by (Poker (Moravcˇ´ık and others 2017) and Bridge (Cazenave actions or rules.
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