Journals / Balkan Journal of Electrical and Computer Engineering / 2020 / Cilt: 8 - Sayı: 2

Artificial Intelligence Based Game Levelling

Pages
147–153
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Thus, the most efficient gaming experience has been transferred to the users.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The applications of artificial intelligence (AI), which is a comprehensive information technology, have been closely related to game technologies.
📊
Key Findings & Data:In this study, the levelling process of a 2-dimensional (2D) platform game has been investigated.
💡
Conclusion & Impact:Thus, the most efficient gaming experience has been transferred to the users.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The applications of artificial intelligence (AI), which is a comprehensive information technology, have been closely related to game technologies. Today, artificial intelligence-based game development applications are increasing their popularity day by day. In this study, the levelling process of a 2-dimensional (2D) platform game has been investigated. The game developed and called “Renga” has a basic gameplay. Game data has been processed through an artificial neural network (ANN), k-nearest neighbour, decision and random tree algorithms and deep learning model that is trained with gameplay and user information. The classification process with the output data provides results for the next game level. In this way, the most effective playability impression that the developers offer to the game users has been created according to game. Furthermore, the variety of difficulty calculated with dynamic data by the user is provided by Renga, in which new sections/levels are created with user-specific assets. Thus, the most efficient gaming experience has been transferred to the users.

🔖

Cite This Article

CETIN, M., SARICA, Y. (2022). Artificial Intelligence Based Game Levelling. Balkan Journal of Electrical and Computer Engineering, Cilt: 8 - Sayı: 2, 147–153. https://ajindex.com/articles/460530

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (1)

  1. Y. Sarica “Game Levelling with Artificial Intelligence.” Master Degree Thesis, Pamukkale University, The Graduate School of Natural and Applied Science, 2019 [2] A. J. Baldwin. “Balancing act: the effect of dynamic difficulty adjustment in competitive multiplayer video games”, 2016. [3] Y. Zhang, S. He, J. Wang, Y. Gao, J. Yang, X. Yu, L. Sha. “Optimizing player's satisfaction through DDA of game AI by UCT for the Game Dead-End”. In Natural Computation, Sixth International Conference on,Vol. 8, 2010, pp. 4161-4165. [4] J. P. Gee. “What video games have to teach us about learning and literacy”. Computers in Entertainment, 1(1), 2003, 20-20. [5] M. Csikszentmihalyi. “Flow and the psychology of discovery and invention”. Harper Perennial, New York, 1997, 39. [6] R. Hunicke. “The case for dynamic difficulty adjustment in games”. In Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology. 2005, pp. 429-433. [7] J. Sinclair. "Feedback control for exergames". Theses: Doctorates and Masters, 2011 [8] M. Kerssemakers, J. Tuxen, J. Togelius, G. N. Yannakakis. “A procedural procedural level generator generator”. In 2012 IEEE Conference on Computational Intelligence and Games, 2012, pp. 335-341. [9] F. Mourato, M. P. dos Santos, F. Birra. “Automatic level generation for platform videogames using genetic algorithms”. In Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, 2011, p. 8. [10] G. Smith, M. Treanor, J. Whitehead, M. Mateas, (). Rhythm-based level generation for 2D platformers. In Proceedings of the 4th International Conference on Foundations of Digital Games, 2009, pp. 175-182). [11] M. Jennings-Teats, G. Smith, N. Wardrip-Fruin. “Polymorph: dynamic difficulty adjustment through level generation”. In Proceedings of the 2010 Workshop on Procedural Content Generation in Games 2010, p. 11. [12] F. Mourato, M. P. dos Santos, F. Birra. “Automatic level generation for platform videogames using genetic algorithms”. In Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology 2011, p. 8 [13] L. Ferreira, C. Toledo. “A search-based approach for generating angry birds levels”. In Computational intelligence and games, 2014. [14] L. Galway, D. Charles, M. Black. “Machine learning in digital games: a survey”. Artificial Intelligence Review, 29(2), 2008, 123-161. [15] P. Spronck, I. Sprinkhuizen-Kuyper, E. Postma. “Online adaptation o