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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18019

Title: Improving Human Players’ T-Spin Skills in Tetris with Procedural Problem Generation
Authors: Oikawa, Taishi
Hsueh, Chu-Hsuan
Ikeda, Kokolo
Keywords: Procedural Content Generation
Training System
Issue Date: 2020-12-20
Publisher: Springer
Magazine name: Lecture Notes in Computer Science
Volume: 12516
Start page: 41
End page: 52
DOI: 10.1007/978-3-030-65883-0_4
Abstract: Researchers in the field of computer games interest in creating not only strong game-playing programs, but also programs that can entertain or teach human players. One of the branches is procedural content generation, aiming to generate game contents such as maps, stories, and puzzles automatically. In this paper, automatically generated puzzles are used to assist human players in improving the playing skills for the game of Tetris, a famous and popular tile-matching game. More specifically, a powerful technique called T-spin is hard for beginners to learn. To assist beginners in mastering the technique, automatically generated two-step to T-spin problems are given for them to solve. Experiments show that the overall ability for beginners to complete T-spin during play is improved after trained by the given problems. The result demonstrates the possibility of using automatically generated problems to assist human players in improving their playing skills.
Rights: This is the author-created version of Springer, Taishi Oikawa, Chu-Hsuan Hsueh and Kokolo Ikeda, Lecture Notes in Computer Science, 12516, 2020, 41-52. The original publication is available at www.springerlink.com, https://doi.org/10.1007/978-3-030-65883-0_4
URI: http://hdl.handle.net/10119/18019
Material Type: author
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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