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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18467

Title: Procedural Maze Generation with Considering Difficulty from Human Players' Perspectives
Authors: Fujihira, Keita
Hsueh, Chu-Hsuan
Ikeda, Kokolo
Keywords: Procedural content generation
Issue Date: 2022-08-01
Publisher: Springer, Cham
Magazine name: Lecture Notes in Computer Science
Volume: 13262
Start page: 165
End page: 175
DOI: 10.1007/978-3-031-11488-5_15
Abstract: In video game development, creating maps, enemies, and many other elements of game levels is one of the important process. In order to improve players' game experiences, game designers need to understand players' behavioral tendencies and create levels accordingly. Among various components of levels, this paper targets mazes and presents an automatic maze generation method with considering difficulty based on human players' tendencies. We first investigate the tendencies using supervised learning and create a test player considering human-likeness by using the tendencies. The test player simulates human players' behaviors when playing mazes and judges difficulty according to the simulation results. Maze evaluation results of subject experiments show that our method succeeds in generating mazes where the difficulty estimated by the test player matches human players'.
Rights: Copyright Springer Nature Switzerland AG 2022. Keita Fujihira, Chu-Hsuan Hsueh, and Kokolo Ikeda, Lecture Notes in Computer Science, vol 13262. Springer, Cham., 2022. The final publication is available at Springer via https://doi.org/10.1007/978-3-031-11488-5_15
URI: http://hdl.handle.net/10119/18467
Material Type: author
Appears in Collections:d11-1. 会議発表論文 (Conference Papers)

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