Fernando de Mesentier Silva
Julian Togelius
ABSTRACT
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.
- Marcus Beard. 2016. A Data Analysis of Board Game Rankings. http://www.bestplay.co/board-games-getting-worse/ Accessed: 2017-02-01.Google Scholar
- Marlene Beyer, Aleksandr Agureikin, Alexander Anokhin, Christoph Laenger, Felix Nolte, Jonas Winterberg, Marcel Renka, Martin Rieger, Nicolas Pflanzl, Mike Preuss, and others. 2016. An integrated process for game balancing. In Computational Intelligence and Games (CIG), 2016 IEEE Conference on. IEEE, 1--8. Google ScholarCross Ref
- Thomas Bidaux. 2017. Kickstarter in 2016 fi Deep dive into the Games category. http://icopartners.com/2017/01/kickstarter-2016-deep-dive-games-category/ Accessed: 2017-03-01.Google Scholar
- Board Game Geek. 2006. Ticket to Ride Strategy - A Complete Strategic overview. http://boardgamegeek.com/thread/142743/strategy-complete-strategic-overview Accessed: 2017-02-01.Google Scholar
- Cameron Browne. 2008. Automatic generation and evaluation of recombination games. Ph.D. Dissertation. Queensland University of Technology.Google Scholar
- Cameron Browne and Frederic Maire. 2010. Evolutionary game design. IEEE Transactions on Computational Intelligence and AI in Games 2, 1 (2010), 1--16. Google ScholarCross Ref
- Murray Campbell, A. Joseph Hoane, and Feng hsiung Hsu. 2002. Deep Blue. Artificial Intelligence 34, 1 (2002), 57 -- 83. http://www.sciencedirect.com/science/article/pii/S0004370201001291Google ScholarDigital Library
- Guillaume Chaslot, Sander Bakkes, Istvan Szita, and Pieter Spronck. 2008. Monte-Carlo Tree Search: A New Framework for Game AI.. In AIIDE.Google Scholar
- Days of Wonder. 2004. Ticket to Ride. https://en.wikipedia.org/wiki/Ticket_to_Ride_(board_game) Accessed: 2016-05-15.Google Scholar
- Fernando de Mesentier Silva, Aaron Isaksen, Julian Togelius, and Andy Nealen. 2016. Generating Heuristics for Novice Players. 2016 IEEE Conference on Computational Intelligence and Games (2016). Google ScholarCross Ref
- Fernando de Mesentier Silva, Scott Lee, Julian Togelius, and Andy Nealen. 2017. AI as Evaluator: Search Driven Playtesting of Modern Board Games. (2017).Google Scholar
- Joris Dormans. 2011. Simulating Mechanics to Study Emergence in Games. Artificial Intelligence in the Game Design Process 2, 6.2 (2011), 5--2.Google ScholarDigital Library
- Milton Griepp. 2016. Hobby Games Market Nearly 1.2 Billion in 2015. http://icv2.com/articles/news/view/35150/hobby-games-market-nearly-1-2-billion Accessed: 2016-10-29.Google Scholar
- Markus Guhe and Alex Lascarides. 2014. Game strategies for the Settlers of Catan. In 2014 IEEE Conference on Computational Intelligence and Games. IEEE, 1--8. Google ScholarCross Ref
- Cathleen Heyden. 2009. Implementing a computer player for Carcassonne. Ph.D. Dissertation. Maastricht University.Google Scholar
- Christoffer Holmgård, Antonios Liapis, Julian Togelius, and Georgios N Yannakakis. 2014. Evolving personas for player decision modeling. In Computational Intelligence and Games (CIG), 2014 IEEE Conference on. IEEE, 1--8.Google ScholarCross Ref
- Vincent Hom and Joe Marks. 2007. Automatic design of balanced board games. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE). 25--30.Google ScholarDigital Library
- Carina Huchler. 2015. An MCTS agent for Ticket to Ride. Master's Thesis. Maastricht University.Google Scholar
- Aaron Isaksen, Dan Gopstein, and Andy Nealen. 2015. Exploring game space using survival analysis. Foundations of Digital Games (2015).Google Scholar
- Aaron Isaksen, Dan Gopstein, Julian Togelius, and Andy Nealen. 2015. Discovering unique game variants. In Computational Creativity and Games Workshop at the 2015 International Conference on Computational Creativity.Google Scholar
- Emil Juul Jacobsen, Rasmus Greve, and Julian Togelius. 2014. Monte mario: platforming with MCTS. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation. ACM, 293--300. Google ScholarDigital Library
- Alexander Jaffe, Alex Miller, Erik Andersen, Yun-EnLiu, Anna Karlin, and Zoran Popovic. 2012. Evaluating Competitive Game Balance with Restricted Play. In AIIDE.Google Scholar
- Niels Justesen, Tobias Mahlmann, and Julian Togelius. 2016. Online Evolution for Multi-action Adversarial Games. In European Conference on the Applications of Evolutionary Computation. Springer, 590--603. Google ScholarCross Ref
- Eva Kraaijenbrink, Frank van Gils, Quan Cheng, Robert van Herk, and Elise van den Hoven. 2009. Balancing skills to optimize fun in interactive board games. In Human-Computer Interaction-INTERACT 2009. Springer, 301--313. Google ScholarDigital Library
- Jonas Krucher. 2015. Algorithmically Balancing a Collectible Card Game. Bachelor's Thesis. ETH Zurich.Google Scholar
- Frank Lantz, Aaron Isaksen, Alexander Jaffe, Andy Nealen, and Julian Togelius. 2017. Depth in Strategic Games. (2017).Google Scholar
- Antonios Liapis, Georgios N Yannakakis, and Julian Togelius. 2013. Sentient Sketchbook: Computer-aided game level authoring.. In FDG. 213--220.Google Scholar
- Tobias Mahlmann, Julian Togelius, and Georgios N Yannakakis. 2012. Evolving card sets towards balancing dominion. In Evolutionary Computation (CEC), 2012 IEEE Congress on. IEEE, 1--8.Google ScholarCross Ref
- Mark J. Nelson. 2011. Game metrics without players: Strategies for understanding game artifacts. In Proceedings of the First Workshop on AI in the Game-Design Process. 14--18.Google Scholar
- Thorbjørn S Nielsen, Gabriella AB Barros, Julian Togelius, and Mark J Nelson. 2015. General video game evaluation using relative algorithm performance profiles. In European Conference on the Applications of Evolutionary Computation. Springer, 369--380.Google ScholarCross Ref
- Michael Pfeiffer. 2004. Reinforcement learning of strategies for Settlers of Catan. In Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education.Google Scholar
- Denis Robilliard, Cyril Fonlupt, and Fabien Teytaud. 2014. Monte-carlo tree search for the game of fi.7 wondersfi. In Computer Games. Springer, 64--77. Google ScholarDigital Library
- Christoph Salge and Tobias Mahlmann. 2010. Relevant information as a formalised approach to evaluate game mechanics. In Computational Intelligence and Games (CIG), 2010 IEEE Symposium on. IEEE, 281--288. Google ScholarCross Ref
- Noor Shaker, Mohammad Shaker, and Julian Togelius. 2013. Ropossum: An Authoring Tool for Designing, Optimizing and Solving Cut the Rope Levels.. In AIIDE.Google ScholarDigital Library
- David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, and others. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529, 7587 (2016), 484--489. Google ScholarCross Ref
- Adam M Smith, Mark J Nelson, and Michael Mateas. 2010. Ludocore: A logical game engine for modeling videogames. In Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games. IEEE, 91--98.Google ScholarCross Ref
- Gillian Smith, Jim Whitehead, and Michael Mateas. 2010. Tanagra: A mixed-initiative level design tool. In Proceedings of the Fifth International Conference on the Foundations of Digital Games. ACM, 209--216. Google ScholarDigital Library
- István Szita, Guillaume Chaslot, and Pieter Spronck. 2009. Monte-carlo tree search in settlers of catan. In Advances in Computer Games. Springer, 21--32.Google Scholar
- Georgios N Yannakakis, Antonios Liapis, and Constantine Alexopoulos. 2014. Mixed-initiative co-creativity. In Proceedings of the 9th Conference on the Foundations of Digital Games.Google Scholar
Index Terms
- AI-based playtesting of contemporary board games
Recommendations
Playtesting with a Purpose
CHI PLAY '16: Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in PlayPlaytesting, or using play to guide game design, gives designers feedback about whether their game is meeting their goals and the player's expectations. We report a case study of designing, deploying, and iterating on a series of playtesting workshops ...
Playtesting for indie studios
AcademicMindtrek '16: Proceedings of the 20th International Academic Mindtrek ConferenceCreating video games is a lengthy and demanding process. Financial success for games studios often depends on making games that deliver a fun and engaging experience for a diverse audience of players. Therefore, understanding how players interact and ...
Gamifying Serious Games: Modding Modern Board Games to Teach Game Potentials
Gaming, Simulation and Innovations: Challenges and OpportunitiesAbstractThe growing interest in game-based approaches is evident. But establishing Gamification and Serious Game processes are challenging, especially when there are few available resources and game design knowledge. Low-tech and low-budget games can be ...
Comments