AI as Evaluator: Search Driven Playtesting of Modern Board Games

This paper presents a demonstration of how AI can be useful in the game design and development process of a modern board game. By using an artificial intelligence algorithm to play a substantial amount of matches of the Ticket to Ride board game and collecting data, we can analyze several features of the gameplay as well as of the game board. Results revealed loopholes in the game’s rules and pointed towards trends in how the game is played. We are then led to the conclusion that large scale simulation utilizing artificial intelligence can offer valuable information regarding modern board games and their designs that would ordinarily be prohibitively expensive or time-consuming to discover manually.

[1]  Ingo Althöfer,et al.  Automatic Generation and Evaluation of Recombination Games , 2010, J. Int. Comput. Games Assoc..

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[26]  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.

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