The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
It might be balanced, but is it actually good? An Empirical Evaluation of Game Level Balancing
Open accessAbstract
Achieving optimal balance in games is essential to their success, yet reliant on extensive manual work and playtesting. To facilitate this process, the Procedural Content Generation via Reinforcement Learning (PCGRL) framework has recently been effectively used to improve the balance of existing game levels. This approach, however, only assesses balance heuristically, neglecting actual human perception. For this reason, this work presents a survey to empirically evaluate the created content paired with human playtesting. Participants in four different scenarios are asked about their perception of changes made to the level both before and after balancing, and vice versa. Based on descriptive and statistical analysis, our findings indicate that the PCGRL-based balancing positively influences players’ perceived balance for most scenarios, albeit with differences in aspects of the balancing between scenarios.
Overview
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
Verification
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?
An additional study with human play testers concludes that balancing simulated with artificial heuristic agents actually improves the perceived balance for humans in most cases.
“An additional study with human play testers concludes that the balancing simulated with artificial heuristic agents actually improves the perceived balance for humans in most cases [16].”
From Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning (2025) · cites this paper as [16]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A law of comparative judgment.
Thurstone, L. (1994) · Psychological Review · doi:10.1037/h0070288 · Cited by 5,077
Experience-Driven Procedural Content Generation
Yannakakis, G. N., & Togelius, J. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.6 · Cited by 484
PCGRL: Procedural Content Generation via Reinforcement Learning
Khalifa, A., Bontrager, P., Earle, S., & Togelius, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v16i1.7416 · Cited by 156
Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent AgentsPreprint
Suárez, J., Du, Y., Isola, P., & Mordatch, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.00784 · Cited by 97
Ashby, T., Webb, B. K., Knapp, G., Searle, J., & Fulda, N. (2023) · International Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581441 · Cited by 85
Demonstrating the Feasibility of Automatic Game Balancing
Volz, V., Rudolph, G., & Naujoks, B. (2016) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2908812.2908913 · Cited by 53
Dungeons & Replicants: Automated Game Balancing via Deep Player Behavior Modeling
Pfau, J., Liapis, A., Volkmar, G., Yannakakis, G. N., & Malaka, R. (2020) · IEEE Conference on Games (CoG) · doi:10.1109/cog47356.2020.9231958 · Cited by 53
What is Game Balancing? - An Examination of Concepts
Becker, A., & Görlich, D. (2020) · ParadigmPlus · doi:10.55969/paradigmplus.v1n1a2 · Cited by 37
Schreiber, I., & Romero, B. (2021) · doi:10.1201/9781315156422 · Cited by 24
Using a Surrogate Model of Gameplay for Automated Level Design
Karavolos, D., Liapis, A., & Yannakakis, G. N. (2018) · IEEE Conference on Computational Intelligence and Games (CIG) · doi:10.1109/cig.2018.8490425 · Cited by 22
RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games
Jeon, H., Baek, I.-C., Bae, C.-M., Park, T., You, W., Ha, T.-G., Jung, H., Noh, J.-H., Oh, S., & Kim, K.-J. (2023) · IEEE Transactions on Games · doi:10.1109/tg.2023.3335399 · Cited by 15
Simulation-Driven Balancing of Competitive Game Levels With Reinforcement Learning
Rupp, F., Eberhardinger, M., & Eckert, K. (2024) · IEEE Transactions on Games · doi:10.1109/tg.2024.3399536 · Cited by 11 · cited 3×
“Similar to our work [10], [11], Karavolos et al. [5] use a PCG method for automated game level design using artificial agents to simulate game play.”
MethodSelf-citationUsing Evolutionary Algorithms to Target Complexity Levels in Game Economies
Rogers, K., Claire, V. L., Frommel, J., Mandryk, R. L., & Nacke, L. E. (2023) · IEEE Transactions on Games · doi:10.1109/tg.2023.3238163 · Cited by 10
Balancing of competitive two-player Game Levels with Reinforcement Learning
Rupp, F., Eberhardinger, M., & Eckert, K. (2023) · 2023 IEEE Conference on Games (CoG) · doi:10.1109/cog57401.2023.10333248 · Cited by 10
Self-citationGEEvo: Game Economy Generation and Balancing with Evolutionary Algorithms
Rupp, F., & Eckert, K. (2024) · 2024 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec60901.2024.10612054 · Cited by 7
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- de, Evolving the Hearthstone Meta
Cited by
Sorted by citations
Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning
Rupp, F., & Eckert, K. (2025) · Proceedings of the 20th International Conference on the Foundations of Digital Games · doi:10.1145/3723498.3723747 · Cited by 1
“An additional study with human play testers concludes that the balancing simulated with artificial heuristic agents actually improves the perceived balance for humans in most cases [16].”
Self-citation
Metadata
- Authors
- Rupp, F., Puddu, A., Becker-Asano, C., & Eckert, K.
- Affiliations
- Univ. of Appl. Sci. Mannheim,Dep. of Computer Science,Mannheim,Germany
- Stuttgart Media University (DE)
- Venue
- 2024 IEEE Conference on Games (CoG)
- Publisher
- IEEE
- Published
- 2024-08-05
- Pages
- 1-4
- OpenAlex
- W4401943467
- Semantic Scholar
- aa365a1381a081229997e7972f4243b9f081791c
- Cited by
- 4
- References
- 15
- Citations by year
- 2026: 1 · 2025: 3
- FWCI
- 1.15
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Conference on Games (CoG) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Volkswagen Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Keywords
- procedural content generation, reinforcement learning, player perceptions, game testing, game design, player experience
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
