Article

Improving Human Players’ T-Spin Skills in Tetris with Procedural Problem Generation

Authors:

Chu-Hsuan Hsueh,

Kokolo IkedaAuthors Info & Claims

Advances in Computer Games: 16th International Conference, ACG 2019, Macao, China, August 11–13, 2019, Revised Selected Papers

Pages 41 - 52

https://doi.org/10.1007/978-3-030-65883-0_4

Published: 11 August 2019 Publication History

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.

References

[1]

De Kegel B and Haahr M Procedural puzzle generation: a survey IEEE Trans. Games 2020 12 1 21-40

[2]

Demediuk, S., Tamassia, M., Raffe, W.L., Zambetta, F., Li, X., Mueller, F.: Monte Carlo tree search based algorithms for dynamic difficulty adjustment. In: 2017 IEEE Conference on Computational Intelligence and Games (CIG 2017), pp. 53–59. IEEE (2017).

[3]

Hirose M, Ito T, and Matsubara H Automatic composition of Tsume-shogi by reverse method J. Jpn. Soc. Artif. Int. 1998 13 3 452-460

[4]

Hunicke, R., Chapman, V.: AI for dynamic difficulty adjustment in games. In: AAAI-04 Workshop on Challenges in Game Artificial Intelligence, pp. 91–96. AAAI Press (2004)

[5]

Ikeda K, Shishido T, and Viennot S Plaat A, van den Herik J, and Kosters W Machine-learning of shape names for the game of Go Advances in Computer Games 2015 Cham Springer 247-259

[6]

Ikeda, K., Viennot, S.: Production of various strategies and position control for Monte-Carlo Go - entertaining human players. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG 2013), pp. 145–152. IEEE (2013).

[7]

Iqbal A Anacleto JC, Fels S, Graham N, Kapralos B, Saif El-Nasr M, and Stanley K Increasing efficiency and quality in the automatic composition of three-move mate problems Entertainment Computing – ICEC 2011 2011 Heidelberg Springer 186-197

[8]

Kapturowski, S., Ostrovski, G., Quan, J., Munos, R., Dabney, W.: Recurrent experience replay in distributed reinforcement learning. In: The Seventh International Conference on Learning Representations (ICLR 2019) (2019)

[9]

Mantere, T., Koljonen, J.: Solving, rating and generating Sudoku puzzles with GA. In: 2007 IEEE Congress on Evolutionary Computation (CEC 2007), pp. 1382–1389. IEEE (2007).

[10]

Mnih V et al. Human-level control through deep reinforcement learning Nature 2015 518 7540 529-533

[11]

Oikawa, T., Ikeda, K.: Procedural problem generation of Tetris for improving T-spin skill. In: The 23rd Game Programming Workshop (GPW-18), pp. 175–182 (2018)

[12]

Oranchak D et al. Di Chio C et al. Evolutionary algorithm for generation of entertaining Shinro logic puzzles Applications of Evolutionary Computation 2010 Heidelberg Springer 181-190

[13]

Ortiz-García EG, Salcedo-Sanz S, Leiva-Murillo JM, Pèrez-Bellido AM, and Portilla-Figueras JA Automated generation and visualization of picture-logic puzzles Comput. Graph. 2007 31 5 750-760

[14]

Schlosser M Computers and chess problem composition ICCA Journal 1988 11 4 151-155

[15]

Sephton, N., Cowling, P.I., Slaven, N.H.: An experimental study of action selection mechanisms to create an entertaining opponent. In: 2015 IEEE Conference on Computational Intelligence and Games (CIG 2015), pp. 122–129. IEEE (2015).

[16]

Shaker, N., Togelius, J., Nelson, M.J.: Procedural Content Generation in Games: A Textbook and an Overview of Current Research. Springer (2016)

[17]

Silver D et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play Science 2018 352 6419 1140-1144

[18]

Takahashi, R.: Mating problem generation of Puyo-Puyo for training. Master thesis, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan (2018)

[19]

Wu, I.C., Wu, T.R., Liu, A.J., Guei, H., Wei, T.h.: On strength adjustment for MCTS-based programs. In: The 33rd AAAI Conference on Artificial Intelligence (AAAI-19). AAAI Press (2019)

Cited By

Withington OCook MTokarchuk L(2024)On the Evaluation of Procedural Level Generation SystemsProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3650016(1-10)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3650016
Dallant JIacono J(2024)How fast can we play Tetris greedily with rectangular pieces?Theoretical Computer Science10.1016/j.tcs.2024.114405992:COnline publication date: 21-Apr-2024
https://dl.acm.org/doi/10.1016/j.tcs.2024.114405
Fujihira KHsueh CIkeda K(2021)Procedural Maze Generation Considering Difficulty from Human Players’ PerspectivesAdvances in Computer Games10.1007/978-3-031-11488-5_15(165-175)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-031-11488-5_15

Index Terms

Improving Human Players’ T-Spin Skills in Tetris with Procedural Problem Generation

Index terms have been assigned to the content through auto-classification.

Recommendations

Playstyles in Tetris: Beyond Player Skill, Score, and Competition
Entertainment Computing – ICEC 2022
Abstract
In this study, we looked at playstyles in Tetris in order to explore how players construct meaning in abstract video games. To do this, we looked at playstyles that go beyond metrics related to player skill, score and competition. Indeed, non-...
Procedural Maze Generation Considering Difficulty from Human Players’ Perspectives
Advances in Computer Games
Abstract
In video game development, creating maps, enemies, and many other elements of game levels is one of the important processes. In order to improve players’ game experiences, game designers need to understand players’ behavioral tendencies and create ...
Towards player-driven procedural content generation
CF '12: Proceedings of the 9th conference on Computing Frontiers

Generating immersive game content is one of the ultimate goals for a game designer. This goal can be achieved by realizing the fact that players' perception of the same game differ according to a number of factors including: players' personality, ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

Advances in Computer Games: 16th International Conference, ACG 2019, Macao, China, August 11–13, 2019, Revised Selected Papers

Aug 2019

193 pages

ISBN:978-3-030-65882-3

DOI:10.1007/978-3-030-65883-0

Editors:
Tristan Cazenave
Université Paris-Dauphine, Paris, France
,
Jaap van den Herik
Leiden University, The Hague, The Netherlands
,
Abdallah Saffidine
The University of New South Wales, Sydney, NSW, Australia
,
I-Chen Wu
National Chiao-Tung University, Hsin-Chu, Taiwan

© Springer Nature Switzerland AG 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 August 2019

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Withington OCook MTokarchuk L(2024)On the Evaluation of Procedural Level Generation SystemsProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3650016(1-10)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3650016
Dallant JIacono J(2024)How fast can we play Tetris greedily with rectangular pieces?Theoretical Computer Science10.1016/j.tcs.2024.114405992:COnline publication date: 21-Apr-2024
https://dl.acm.org/doi/10.1016/j.tcs.2024.114405
Fujihira KHsueh CIkeda K(2021)Procedural Maze Generation Considering Difficulty from Human Players’ PerspectivesAdvances in Computer Games10.1007/978-3-031-11488-5_15(165-175)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-031-11488-5_15

View Options

View options

Figures

Tables

Media

View Table of Conten