Players Adaptive Monster Generation Technique Using Genetic Algorithm

Ji-Min Kim; Sun-Jeong Kim; Seokmin Hong

Players Adaptive Monster Generation Technique Using Genetic Algorithm

Ji-Min Kim, Sun-Jeong Kim, Seokmin Hong, Journal of Internet Computing and Services, Vol. 18, No. 2, pp. 43-52, Apr. 2017

10.7472/jksii.2017.18.2.43, Full Text:

Keywords: Artificial intelligence, genetic algorithm, Procedural Content Generation, Players Adaptive Monster Generation

Abstract

As the game industry is blooming, the generation of contents is far behind the consumption of contents. With this reason, it is necessary to afford the game contents considering level of game player's skill. In order to effectively solve this problem, Procedural Content Generation(PCG) using Artificial Intelligence(AI) is one of the plausible options. This paper proposes the procedural method to generate various monsters considering level of player's skill using genetic algorithm. One gene consists of the properties of a monster and one genome consists of genes for various monsters. A generated monster is evaluated by battle simulation with a player and then goes through selection and crossover steps. Using our proposed scheme, players adaptive monsters are generated procedurally based on genetic algorithm and the variety of monsters which are generated with different number of genome is compared.

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Cite this article

[APA Style]

Kim, J., Kim, S., & Hong, S. (2017). Players Adaptive Monster Generation Technique Using Genetic Algorithm. Journal of Internet Computing and Services, 18(2), 43-52. DOI: 10.7472/jksii.2017.18.2.43.

[IEEE Style]

J. Kim, S. Kim, S. Hong, "Players Adaptive Monster Generation Technique Using Genetic Algorithm," Journal of Internet Computing and Services, vol. 18, no. 2, pp. 43-52, 2017. DOI: 10.7472/jksii.2017.18.2.43.

[ACM Style]

Ji-Min Kim, Sun-Jeong Kim, and Seokmin Hong. 2017. Players Adaptive Monster Generation Technique Using Genetic Algorithm. Journal of Internet Computing and Services, 18, 2, (2017), 43-52. DOI: 10.7472/jksii.2017.18.2.43.