Abstract
In recent years, many researchers are attracted to computer games research. Capable gamers can easily get bored, while beginners tend to give up after trying several times because the game does not correspond to their level of interest. Therefore, this paper proposes that the user’s play pattern to be modeled on the basis of probability and level designer will dynamically generates the gaming level accordingly. We analyze user’s play pattern and design pattern based on GMM (probability model) and dynamically generate the level with online learning technique adapting the reinforcement technique. The play pattern is modeled using GMM and in order to create game level dynamically, the method of updating the weight of enemy creation using online script is proposed. Finally, we apply our proposed method to a 2D shooting game and introduce user’s play pattern leading to design pattern in the game.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Falstein, N.: Game Developer Magazine, The Flow Channel (2004)
Koster, R.: Theory of Fun for Game Design. Paraglyph Press, Phoenix (2004)
Johnson, D., Wiles, J.: Effective Affective User Interface Design in Games. In: International Conference on Affective Human Factors Design, Singapore (2003)
Laired, J.E.: Using a Computer Game to Develop Advanced AI, pp. 70–75. IEEE Computer Society Press, Los Alamitos (2001)
Freisleben, B.: A Neural Network that Learns to Play Five-in-a-Row. In: International Conference on Artificial Neural Networks and Expert Systems, pp. 87–90 (1995)
Faybish, I.: Applying the Genetic Algorithm to the Game of Othello, Master’s thesis, Vrije Universiteit Brussel, Computer Science Department, Brussels, Belgium (1999)
Moon, T.K.: The Expectation-Maximization Algorithm. IEEE Signal Processing 13, 47–60 (1996)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobword: Image Segmentaion Using Expectation-Maximization and Its Application to Image Querying. IEEE Trans. On Pattern Recognition and Machine Analysis 24(8), 1026–1038 (2002)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. p. 55. John Wiley & Sons Inc, Chichester (2001)
Ghory, I.: Reinforcement learning in board games, Technical Report CSTR-04-004, Department of Computer Science, University of Bristol, (May 2004)
Spronck, P., Sprinkhuizen-Kuyper, I., Postma, E.: Difficulty Scaling of Game AI. In: International Conference on Intelligent Games and Simulation, Belgium, pp. 33–37 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Yang, J., Min, S., Wong, CO., Kim, J., Jung, K. (2007). Dynamic Game Level Generation Using On-Line Learning. In: Hui, Kc., et al. Technologies for E-Learning and Digital Entertainment. Edutainment 2007. Lecture Notes in Computer Science, vol 4469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73011-8_88
Download citation
DOI: https://doi.org/10.1007/978-3-540-73011-8_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73010-1
Online ISBN: 978-3-540-73011-8
eBook Packages: Computer ScienceComputer Science (R0)