Evolving player-specific content for level based arcade games
Pages 329 - 330
Abstract
In this poster, we attempt to generate content for single player level based arcade games, according to the player's skill. The objective is to make the game just enough challenging for the player. An evolvable representation of the game content is developed. The player's performance is measured after completion of each level. An Artificial Neural Network (ANN) is used to estimate the expected performance for the level content. The deviation of the actual performance of the player from the expected performance is used to estimate the toughness of the next level. A Genetic Algorithm then evolves the next level. The fittest candidate is the one matching the required toughness.
References
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Koster, R. A theory of fun for game design. Paraglyph press, 2004.
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Malone, T. W. What makes things fun to learn? Heuristics for designing instructional computer games. In Proceedings of the 3rd ACM SIGSMALL symposium and the first SIGPC symposium on Small systems, Palo Alto, California, US, September 1980, 162--169.
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Togelius, J., De Nardi, R., Lucas, S. M. Towards automatic personalised content creation in racing game. In Proceedings of the IEEE Symposium on Computational Intelligence and Games, Honolulu, US, April 2007, 252--255.
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Verma, M. A. and McOwan, P. W. An adaptive methodology for synthesising mobile phone games using genetic algorithms. In Proceedings of the Congress on Evolutionary Computation, Edinburgh, UK, September 2005, 528--535.
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Yannakakis, G. N. AI in Computer Games: Generating Interesting Interactive Opponents by the use of Evolutionary Computation. PhD thesis, University of Edinburg, 2005.
Index Terms
- Evolving player-specific content for level based arcade games
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Published In
April 2009
386 pages
ISBN:9781605584379
DOI:10.1145/1536513
- Conference Chair:
- Jim Whitehead,
- Program Chair:
- R. Michael Young
Copyright © 2009 ACM.
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- SASDG: The Society for the Advancement of the Science of Digital Games
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- SIGMOD: ACM Special Interest Group on Management of Data
- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
- SIGCSE: ACM Special Interest Group on Computer Science Education
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 April 2009
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- Poster
Conference
FDG '09
Sponsor:
- SASDG
FDG '09: International Conference on the Foundations of Digital Games
April 26 - 30, 2009
Florida, Orlando
Acceptance Rates
Overall Acceptance Rate 152 of 415 submissions, 37%
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