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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 顏嗣鈞 | zh_TW |
dc.contributor.advisor | Hsu-Chun Yen | en |
dc.contributor.author | 林子祺 | zh_TW |
dc.contributor.author | Tzu-Chi Lin | en |
dc.date.accessioned | 2023-08-09T16:31:02Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-09 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-25 | - |
dc.identifier.citation | Alchyr. Basicmod. GitHub repository, 2022.
Bay 12 Games. Dwarf Fortress. 2006. Bernkastel. How to design a ”soulslike” map? 2021. B. Cowley, D. Charles, M. Black, and R. Hickey. Toward an understanding of flow in video games. Comput. Entertain., 6:1–27, 07 2008. B. De Kegel and M. Haahr. Procedural puzzle generation: A survey. IEEE Transactions on Games, 12(1):21–40, 2019. J. Doran and I. Parberry. A prototype quest generator based on a structural analysis of quests from four mmorpgs. Proceedings of the 2nd International Workshop on Procedural Content Generation in Games, 2011. J. Dormans. Adventures in level design: generating missions and spaces for ac tion adventure games. Proceedings of the 2010 workshop on procedural content generation in games, pages 1–8, 2010. J. Dormans. Cyclic Generation. Procedural Generation in Game Design, pages 83–95, 2017. J. Freiknecht. Procedural content generation for games. 2021. M. Gardner. The fantastic combinations of john conway’s new solitaire game’life. Sc. Am., 223:20–123, 1970. L. Johnson, G. N. Yannakakis, and J. Togelius. Cellular automata for real-time gen eration of infinite cave levels. Proceedings of the 2010 Workshop on Procedural Content Generation in Games, pages 1–4, 2010. MegaCrit. Slay the Spire. 2016. A. B. Moghadam and M. K. Rafsanjani. A genetic approach in procedural content generation for platformer games level creation. 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), pages 141–146, 2017. Mojang Studios. Minecraft. 2011. Nintendo. Super Mario Bros. 1985. Nintendo. The Legend of Zelda: A Link to the Past. 1991. F. Paradis. Gdc vault - procedural generation of cinematic dialogues in ’assassin’s creed odyssey’. 2019. C. Politowski. Improving Video Game Balance Testing Using Autonomous Agents. PhD thesis, Concordia University, 2022. J. Schrum, J. Gutierrez, V. Volz, J. Liu, S. Lucas, and S. Risi. Interactive evolution and exploration within latent level-design space of generative adversarial networks. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pages 148–156, 2020. A. Summerville and M. Mateas. Sampling hyrule: Multi-technique probabilistic level generation for action role playing games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 11(3):63–67, 2015. J. Togelius and J. Schmidhuber. An experiment in automatic game design. 2008 IEEE Symposium On Computational Intelligence and Games, pages 111–118, 2008. J. Togelius, G. Yannakakis, K. Stanley, and C. Browne. Search-based procedural content generation. pages 141–150, 04 2010. M. Toy, G. Wichman, K. Arnold, and J. Lane. Rogue. 1980. K. White. Implementation of a procedural level generation engine for developing rouge-like dungeons. 05 2018. H. Yu and T. Trawick. Personalized procedural content generation to mini mize frustration and boredom based on ranking algorithm. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 7(1):208–213, 2011. L. Yujian and L. Bo. A normalized levenshtein distance metric. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6):1091–1095, 2007 | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88318 | - |
dc.description.abstract | 我們提出了一種名為「難度控制程序化生成」(DC-PCG) 的程序化內容生成演算法,用於生成電子遊戲關卡。我們的研究有兩個主要貢獻。首先,我們設計了一系列用於衡量電子遊戲關卡難度的標準。其次,DC-PCG 利用這些標準來控制生成關卡的難度。DC-PCG 使用基於搜索的演算法在生成空間中搜索能量最低的關卡。DC-PCG 包括兩個階段。第一階段,DC-PCG 生成關卡的拓撲結構。第二階段,DC-PCG 為關卡中的每個節點生成遭遇內容。對於每個階段,我們有不同的能量函數。我們透過調整能量函數中的參數來控制生成關卡的難度。我們進行了一項使用者研究,以了解生成內容的品質。在這項研究中,參與者玩了由DC-PCG 生成的關卡,並給這些遊戲關卡評分。通過分析使用者研究的數據,我們確定 DC-PCG 生成的關卡難度與使用者研究中的評分相符。這表明 DC-PCG 能夠生成具有可控難度的高品質電子遊戲關卡。 | zh_TW |
dc.description.abstract | We propose Difficulty Control Procedural Content Generation(DC-PCG), a procedural content generation algorithm that generates video game levels. Our research has two primary contributions. First, we design a series of metrics to measure the difficulty of the video game level. Second, DC-PCG uses these metrics to control the difficulty of the generated level. DC-PCG uses a search-based algorithm to search for the level with the lowest energy in the generation space. DC-PCG has two-phase. In the first phase, DC-PCG generates the topological structure of the level. In the second phase, DC-PCG generates the encounter for every vertex in the level. For each phase, we have different energy functions. We control the difficulty of the generated level by tweaking the parameters in the energy functions. We conduct a user study to understand the quality of the generated content. In the study, participants played the level generated by DC-PCG and scored the game levels. After analyzing the data from the user study, we determine that the difficulty of the level generated by DC-PCG aligns with the user’s score in the user study. It shows that DC-PCG can produce high-quality video game levels with controlled difficulty. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-09T16:31:02Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-09T16:31:02Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | Acknowledgements ii
摘要 iii Abstract iv Contents vi List of Figures ix List of Tables xi Chapter 1 Introduction 1 Chapter 2 Related Work 3 2.1 Procedural Content Generation Attributes 4 2.1.1 Online versus Offline 4 2.1.2 Necessary versus Optional Content 4 2.1.3 Random Seeds versus Parameter Vectors 5 2.1.4 Stochastic versus Deterministic Generation 5 2.1.5 Constructive versus Generate-and-test 5 2.2 Procedural Content Generation Technique 6 2.2.1 Cellular Automaton 6 2.2.2 Generative Grammars 6 2.2.3 Search-Based 7 Chapter 3 Method 9 3.1 Slay The Spire Level Structure 9 3.1.1 Vertex Property 9 3.1.2 Edge Property 11 3.2 Slay The Spire Basic Gameplay 12 3.2.1 Choose The Character 13 3.2.2 Deck Building 13 3.2.3 Navigate The Level 13 3.2.4 Resource Management 14 3.2.5 Decision Making 15 3.3 Pipeline 15 Chapter 4 Implementation 17 4.1 Environment 17 4.2 Notation 17 4.2.1 Playable 18 4.2.2 Candidate 18 4.3 Phase 1: Generate Graph Structure 19 4.3.1 Cut Set Energy Function 19 4.3.2 Add Random Path Function 20 4.3.3 Remove Random Path Function 20 4.4 Phase 2: Generate Vertex Symbol 20 4.4.1 Survival Energy Function 21 4.4.2 Levenshtein Distance Energy Function 22 4.4.3 Repeated Vertices Energy Function 23 4.4.4 Change Symbol Function 24 Chapter 5 Analysis 31 5.1 User Study 32 5.1.1 Level Selection 32 5.1.2 Environment 34 5.1.3 Procedure 34 5.1.4 Questionnaire Design 35 5.1.5 Participants 37 5.1.6 Discussion 38 5.2 Performance 41 Chapter 6 Conclusion 43 References 44 | - |
dc.language.iso | en | - |
dc.title | 具有難度控制功能之電子遊戲程序化內容生成器 | zh_TW |
dc.title | A Procedural Content Generator with Difficulty Control for Video Games | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 雷欽隆;郭斯彥;黃士嘉 | zh_TW |
dc.contributor.oralexamcommittee | Chin-Laung Lei;Sy-Yen Kuo;Shih-Chia Huang | en |
dc.subject.keyword | 程序化內容生成,圖論,電子遊戲, | zh_TW |
dc.subject.keyword | Prodecural content generation,Graph theory,Video game, | en |
dc.relation.page | 46 | - |
dc.identifier.doi | 10.6342/NTU202301861 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-07-27 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 電機工程學系 | - |
顯示於系所單位: | 電機工程學系 |
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