Artificial Intelligence Based Game Levelling

Abstract

The game development process is becoming a more detailed structure every day. The applications of artificial intelligence (AI), which is a comprehensive information technology, have been closely related to game technologies. In this study, the levelling process of a 2-dimensional (2D) platform game was 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.

Keywords

• Artificial Intelligence,Difficulty Adjustment,Content Generation,k-Nearest Neighbor,Random Forest,Artificial Neural Networks

References

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 of game opponent AI in simulation and in practice”. In Proceedings of the 4th International Conference on Intelligent Games and Simulation, 2003, pp. 93-100.
16. D. Johnson, J. Wiles. “Computer games with intelligence”. In Fuzzy Systems. The 10th IEEE International Conference on, Vol. 3, 2001, pp. 1355-1358.
17. M. Persson. “Infinite Mario bros”. 2008, Online Game.
18. W. Baghdadi, F. S. Eddin, R. Al-Omari, Z. Alhalawani, M. Shaker, N. Shaker. “A procedural method for automatic generation of spelunky levels”. In European Conference on the Applications of Evolutionary Computation, 2015, pp. 305-317.
19. 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.
20. V. der Linden, R. R. Lopes, R. Bidarra, “Designing procedurally generated levels”, In Proceedings of the second workshop on Artificial Intelligence in the Game Design Process, 2013.
21. G. N. Yannakakis, J. Togelius. “A panorama of artificial and computational intelligence in games”. IEEE Transactions on Computational Intelligence and AI in Games, 7(4), 2014, 317-335.
22. S. Woodcock, J. E. Laird, D. Pottinger, “Game AI: The state of the industry”. Game Developer Magazine, 8,c2000.
23. P. Spronck, I. Sprinkhuizen-Kuyper, E. Postma. “Difficulty scaling of game AI”. In Proceedings of the 5th International Conference on Intelligent Games and Simulation, 2004, pp. 33-37.
24. S. Lee, K. Jung. “Dynamic game level design using gaussian mixture model”. In Pacific Rim International Conference on Artificial Intelligence, 2006, pp. 955-959.
25. P. Spronck, M. Ponsen, I. Sprinkhuizen-Kuyper, E. Postma. “Adaptive game AI with dynamic scripting”. Machine Learning, 63(3), 2006, 217-248.
26. S. L. Kent. “The Ultimate History of Video Games: From Pong to Pokemon-The Story Behind the Craze That Touched Our Lives and Changed the World”, 2001, New York: Three Rivers Press.
27. J. Togelius, S. Karakovskiy, J. Koutník, J. Schmidhuber. “Super Mario Evolution. In Computational Intelligence and Games”, IEEE Symposium, CIG 2009, pp. 156-161.
28. J. K. Haas. “A History of the Unity Game Engine”, 2014.
29. B. Tay, J. K. Hyun, S. Oh. “A machine learning approach for specification of spinal cord injuries using fractional anisotropy values obtained from diffusion tensor images”. Computational and mathematical methods in medicine, 2014.
30. L. Breiman. “Random forests”. Machine learning, 45(1), 2001, 5-32.
31. N. Sirikulviriya, S. Sinthupinyo. “Integration of rules from a random forest”. In International Conference on Information and Electronics Engineering, Vol. 6, 2011pp. 194-198.
32. Y. LeCun, Y. Bengio, G. Hinton. “Deep learning”. Nature, 521(7553), 2015, 436-444.
33. X. Yao. “Evolving artificial neural networks”. Proceedings of the IEEE, 87(9), 1999, 1423-1447.
34. H. Guo, H. Nguyen, D. A. Vu, X. N. Bui. “Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach”. Resources Policy, 101474, 2019.