Automated Playtesting of Matching Tile Games
Open accessAbstract
Matching tile games are an extremely popular game genre. Arguably the most popular iteration, Match-3 games, are simple to understand puzzle games, making them great benchmarks for research. In this paper, we propose developing different procedural personas for Match-3 games in order to approximate different human playstyles to create an automated playtesting system. The procedural personas are realized through evolving the utility function for the Monte Carlo Tree Search agent. We compare the performance and results of the evolution agents with the standard Vanilla Monte Carlo Tree Search implementation as well as to a random move-selection agent. We then observe the impacts on both the game's design and the game design process. Lastly, a user study is performed to compare the agents to human play traces.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Personas enabled distinct playstyles and helped to playtest a game like distinct players.
“As a result, personas enabled distinct playstyles and helped to playtest a game like distinct players.”
From Playtesting: What is Beyond Personas (2023) · cites this paper as [5]
Procedural personas can diversify learned policies by rewiring the feedback mechanism with a utility function.
“Procedural personas [4] [5] accomplish this by rewiring the feedback mechanism by a utility function.”
From Playtesting: What is Beyond Personas (2023) · cites this paper as [5]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Artificial Intelligence and Games
Yannakakis, G. N., & Togelius, J. (2018) · doi:10.1007/978-3-319-63519-4 · Cited by 511 · see citing works
Self-citationAutomated Playtesting With Procedural Personas Through MCTS With Evolved Heuristics
Holmgard, C., Green, M. C., Liapis, A., & Togelius, J. (2019) · IEEE Transactions on Games · doi:10.1109/tg.2018.2808198 · Cited by 125 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 8
- C. Holmgård, A. Liapis, J. Togelius, and G. N. Yannakakis, "Evolving personas for player decision modeling," in 2014 IEEE Conference on Computational Intelligence and Games (CIG), Aug 2014, pp. 1-8.
- B. Tastan and G. Sukthankar, "Learning policies for first person shooter games using inverse reinforcement learning," in Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, ser. AIIDE'11. AAAI Press, 2011, pp. 85-90. [Online]. Available: http://dl.acm.org/citation.cfm?id=3014589.3014604
- A. Tychsen and A. Canossa, "Defining personas in games using metrics," in Proceedings of the 2008 Conference on Future Play: Research, Play, Share, ser. Future Play '08. New York, NY, USA: ACM, 2008, pp. 73- 80. [Online]. Available: http://doi.acm.org/10.1145/1496984.1496997 · DOI
- A. Drachen and A. Canossa, "Patterns of play: Play-personas in user- centred game development," in Proceedings of DiGRA 2009. DIGRA, 2009.
- C. Holmgård, A. Liapis, J. Togelius, and G. N. Yannakakis, "Generative agents for player decision modeling in games," in Poster Proceedings of the 9th Conference on the Foundations of Digital Games (FDG), 2014.
- C. Browne, E. Powley, D. Whitehouse, S. Lucas, P. Cowling, P. Rohlf- shagen, S. Tavener, D. Perez Liebana, S. Samothrakis, and S. Colton, "A survey of monte carlo tree search methods," IEEE Transactions on Computational Intelligence and AI in Games (TCIAIG), vol. 4:1, pp. 1-43, 03 2012.
- A. Benbassat and M. Sipper, "Evomcts: Enhancing mcts-based players through genetic programming," in 2013 IEEE Conference on Computa- tional Intelligence in Games (CIG), Aug 2013, pp. 1-8.
- L. Kocsis and C. Szepesvári, "Bandit based monte-carlo planning," in Machine Learning: ECML 2006, J. Fürnkranz, T. Scheffer, and M. Spiliopoulou, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 282-293.
Cited by
Sorted by citations
Playtesting: What is Beyond Personas
Ariyurek, S., Surer, E., & Betin-Can, A. (2023) · IEEE Transactions on Games · doi:10.1109/tg.2022.3165882 · Cited by 7 · see citing works
Generating Personas for Games with Multimodal Adversarial Imitation Learning
Ahlberg, W., Sestini, A., Tollmar, K., & Gisslén, L. (2023) · 2023 IEEE Conference on Games (CoG) · doi:10.1109/cog57401.2023.10333167 · Cited by 5 · see citing works
UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors
Cilan, A., & Ozgovde, A. (2026) · IEEE Transactions on Games · doi:10.1109/tg.2026.3703355
“Traditional methods typically depend on predefined rules for generating player archetypes, such as killer, defender, and explorer [16], [17], [18].”
Expressive Response Curves: Testing Expressive Game Feel with A*
Junius, N., & Carstensdottir, E. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v19i1.27524
UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors
Cilan, A., & Özgövde, A. (2025) · arXiv
- Authors
- Mugrai, L., Silva, F., Holmgard, C., & Togelius, J.
- Affiliations
- New York University (US)
- Venue
- 2019 IEEE Conference on Games (CoG)
- Publisher
- IEEE
- Published
- 2019-08
- Pages
- 1-7
- OpenAlex
- W2962554883
- Cited by
- 28
- References
- 2
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 1 · 2020: 8 · 2019: 2
- FWCI
- 1.50
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Conference on Games (CoG) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Keywords
- Monte Carlo tree search, game design, puzzle games, game design process
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
