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Procedural Content Generation in Game Development Process

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Języki publikacji
EN
Abstrakty
EN
This article describes procedural content generation algorithms used by an independent video game developer in a level design process for the logical game Keri Tap. Genetic algorithms were used as the computational core of the level generation routines. The research that was carried out in order to defi ne good algorithm setup has been described. Main idea of this article is to show that PCG [14] methods can be used by small independent video game developers to gain measurable benefits.
Rocznik
Strony
7--10
Opis fizyczny
Bibliogr. 16 poz., wykr., rys.
Twórcy
  • Warsaw University of Technology, The Faculty of Electronics and Information Technology ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Bibliografia
  • [1] Alon Itai, C.H.P., and J.L. Szwarcfi ter. “Hamilton paths in grid graphs”. Society for Industrial and Applied Mathematics 11 (4), 1982: 676–686.
  • [2] Arabas, J. “Predicting genetic diversity of populations in evolutionary search in R1 ”. Proceedings of the 2011 Evolutionary Computation and Global Optimization conference, 2011.
  • [3] Arkin, E.M., et al. Not being (super)thin or solid is hard: A study of grid hamiltonicity, 2008.
  • [4] Ashlock, D., C. Lee, and C. McGuinness. “Search-based procedural generation of maze-like levels”. IEEE Trans. Comput. Intellig. and AI in Games 3 (3), 2011: 260–273.
  • [5] Buckland, M., and M. Collins. AI Techniques for Game Programming. Premier Press, 2002.
  • [6] Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning. 1st ed. Boston, MA, USA:Addison-Wesley Longman Publishing Co., Inc., 1989.
  • [7] Gudlaugsson, B. Procedural Content Generation. Reykjavik University, 2006.
  • [8] Juul, J. “The open and the closed: Game of emergence and games of progression”. In Proc. Computer Game and Digital Cultures, 2002: 323–329.
  • [9] Moraglio, A., J. Togelius, and S.M. Lucas. “Product geometric crossover for the Sudoku puzzle”. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 2006.
  • [10] Nielsen, J. Participation inequality: Encouraging more users to contribute, 2006.
  • [11] Ondrejka, C., Ed. Escaping the Gilded Cage: User Created Content and Building the Metaverse. New York Law School, 2003.
  • [12] O’reilly, T. What is web 2.0? design patterns and business models for the next generation of software.
  • [13] Prusinkiewicz, P., and A. Lindenmayer. The algorithmic beauty of plants. New York, NY, USA: Springer-Verlag New York, Inc., 1996.
  • [14] Togelius, J., et al. “Search-based procedural content generation: A taxonomy and survey”. IEEE Trans. Comput. Intellig. and AI in Games 3 (3), 2011: 172--186.
  • [15] Whitley, d., T. Starkweather, and D. Shaner “The traveling salesman and sequence scheduling: Quality solutions using genetic edge recombination”. In: In Handbook of Genetic Algorithms, 1990: 350–372.
  • [16] Wirth, N. Algorithms + Data Structures = Programs. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1978.
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Bibliografia
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