Computer Aided Content Generation: A Gloomhaven Case Study

Marcus Gerhold; Kristian Tijben

doi:10.1145/3582437.3587196

Computer Aided Content Generation: A Gloomhaven Case Study

Marcus Gerhold, Kristian Tijben

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

We present how an evolutionary algorithm can be used to generate scenarios for the board game Gloomhaven. The scenarios are evaluated according to size, difficulty, thematic coherence, complexity and layout. We encode the game's default scenarios into textual descriptions and use them as initial population for the algorithm. Our dungeon generation works within the confines given by the physical board game, i.e., special attention is given to availability of game pieces and map tiles. The generated dungeons can be constructed without overlapping tiles.

Original language	English
Title of host publication	Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023
Editors	Phil Lopes, Filipe Luz, Antonios Liapis, Henrik Engstrom
Publisher	Association for Computing Machinery
Number of pages	10
ISBN (Electronic)	978-1-4503-9856-5, 978-1-4503-9855-8
DOIs	https://doi.org/10.1145/3582437.3587196
Publication status	Published - 12 Apr 2023
Event	18th International Conference on the Foundations of Digital Games, FDG 2023 - Lisbon, Portugal Duration: 11 Apr 2023 → 14 Apr 2023 Conference number: 18

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	18th International Conference on the Foundations of Digital Games, FDG 2023
Abbreviated title	FDG 2023
Country/Territory	Portugal
City	Lisbon
Period	11/04/23 → 14/04/23

Keywords

board game
dungeon generation
Evolutionary algorithm
Gloomhaven

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10.1145/3582437.3587196Licence: CC BY

3582437.3587196Final published version, 2.56 MBLicence: CC BY

Cite this

Gerhold, M., & Tijben, K. (2023). Computer Aided Content Generation: A Gloomhaven Case Study. In P. Lopes, F. Luz, A. Liapis, & H. Engstrom (Eds.), Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023 Article 61 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3582437.3587196

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abstract = "We present how an evolutionary algorithm can be used to generate scenarios for the board game Gloomhaven. The scenarios are evaluated according to size, difficulty, thematic coherence, complexity and layout. We encode the game's default scenarios into textual descriptions and use them as initial population for the algorithm. Our dungeon generation works within the confines given by the physical board game, i.e., special attention is given to availability of game pieces and map tiles. The generated dungeons can be constructed without overlapping tiles.",

keywords = "board game, dungeon generation, Evolutionary algorithm, Gloomhaven",

author = "Marcus Gerhold and Kristian Tijben",

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Gerhold, M & Tijben, K 2023, Computer Aided Content Generation: A Gloomhaven Case Study. in P Lopes, F Luz, A Liapis & H Engstrom (eds), Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023., 61, ACM International Conference Proceeding Series, Association for Computing Machinery, 18th International Conference on the Foundations of Digital Games, FDG 2023, Lisbon, Portugal, 11/04/23. https://doi.org/10.1145/3582437.3587196

Computer Aided Content Generation: A Gloomhaven Case Study. / Gerhold, Marcus; Tijben, Kristian.
Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023. ed. / Phil Lopes; Filipe Luz; Antonios Liapis; Henrik Engstrom. Association for Computing Machinery, 2023. 61 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - We present how an evolutionary algorithm can be used to generate scenarios for the board game Gloomhaven. The scenarios are evaluated according to size, difficulty, thematic coherence, complexity and layout. We encode the game's default scenarios into textual descriptions and use them as initial population for the algorithm. Our dungeon generation works within the confines given by the physical board game, i.e., special attention is given to availability of game pieces and map tiles. The generated dungeons can be constructed without overlapping tiles.

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Computer Aided Content Generation: A Gloomhaven Case Study

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