AI-based playtesting of contemporary board games

Ticket to Ride is a popular contemporary board game for two to four players, featuring a number of expansions with additional maps and tweaks to the core game mechanics. In this paper, four different game-playing agents that embody different playing styles are defined and used to analyze Ticket to Ride. Different playing styles are shown to be effective depending on the map and rule variation, and also depending on how many players play the game. The performance profiles of the different agents can be used to characterize maps and identify the most similar maps in the space of playstyles. Further analysis of the automatically played games reveal which cities on the map are most desirable, and that the relative attractiveness of cities is remarkably consistent across numbers of players. Finally, the automated analysis also reveals two classes of failures states, where the agents find states which are not covered by the game rules; this is akin to finding bugs in the rules. We see the analysis performed here as a possible template for AI-based playtesting of contemporary board games.

[1]  Michael Pfeiffer,et al.  Reinforcement Learning of Strategies for Settlers of Catan , 2004 .

[2]  Denny Somach,et al.  Ticket to Ride , 1989 .

[3]  Michael Mateas,et al.  LUDOCORE: A logical game engine for modeling videogames , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[4]  Elise van den Hoven,et al.  Balancing Skills to Optimize Fun in Interactive Board Games , 2009, INTERACT.

[5]  J. Togelius,et al.  Discovering Unique Game Variants , 2015 .

[6]  Pieter Spronck,et al.  Monte-Carlo Tree Search in Settlers of Catan , 2009, ACG.

[7]  Pieter Spronck,et al.  Monte-Carlo Tree Search: A New Framework for Game AI , 2008, AIIDE.

[8]  Cyril Fonlupt,et al.  Monte-Carlo Tree Search for the Game of "7 Wonders" , 2014, CGW@ECAI.

[9]  Julian Togelius,et al.  Monte Mario: platforming with MCTS , 2014, GECCO.

[10]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[11]  Julian Togelius,et al.  Evolving card sets towards balancing dominion , 2012, 2012 IEEE Congress on Evolutionary Computation.

[12]  Frédéric Maire,et al.  Evolutionary Game Design , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[13]  Michael Mateas,et al.  Tanagra: a mixed-initiative level design tool , 2010, FDG.

[14]  Cathleen Heyden,et al.  IMPLEMENTING A COMPUTER PLAYER FOR CARCASSONNE , 2009 .

[15]  Andrew Nealen,et al.  Exploring Game Space Using Survival Analysis , 2015, FDG.

[16]  Joe Marks,et al.  Automatic Design of Balanced Board Games , 2007, AIIDE.

[17]  Christoph Salge,et al.  Relevant Information as a formalised approach to evaluate game mechanics , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[18]  Alex Lascarides,et al.  Game strategies for The Settlers of Catan , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[19]  Antonios Liapis,et al.  Mixed-initiative co-creativity , 2014, FDG.

[20]  Julian Togelius,et al.  Evolving personas for player decision modeling , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[21]  Julian Togelius,et al.  Generating heuristics for novice players , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

[22]  Mark J. Nelson.  Game Metrics Without Players: Strategies for Understanding Game Artifacts , 2011, Artificial Intelligence in the Game Design Process.

[23]  Murray Campbell,et al.  Deep Blue , 2002, Artif. Intell..

[24]  Zoran Popovic,et al.  Evaluating Competitive Game Balance with Restricted Play , 2012, AIIDE.

[25]  Julian Togelius,et al.  Depth in Strategic Games , 2017, AAAI Workshops.

[26]  Julian Togelius,et al.  AI as Evaluator: Search Driven Playtesting of Modern Board Games , 2017, AAAI Workshops.

[27]  C Allen.  Ticket to ride. , 1993, Nursing times.

[28]  Julian Togelius,et al.  Ropossum: An Authoring Tool for Designing, Optimizing and Solving Cut the Rope Levels , 2013, AIIDE.

[29]  Cameron Browne,et al.  Automatic generation and evaluation of recombination games , 2008 .

[30]  Joris Dormans,et al.  Simulating Mechanics to Study Emergence in Games , 2011, IDP@AIIDE.

[31]  Julian Togelius,et al.  Sentient Sketchbook: Computer-aided game level authoring , 2013, FDG.

[32]  Mike Preuss,et al.  An integrated process for game balancing , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

[33]  Julian Togelius,et al.  General Video Game Evaluation Using Relative Algorithm Performance Profiles , 2015, EvoApplications.

[34]  Julian Togelius,et al.  Online Evolution for Multi-action Adversarial Games , 2016, EvoApplications.