research-article

Towards multiobjective procedural map generation

Authors:

Julian Togelius,

Georgios N. YannakakisAuthors Info & Claims

PCGames '10: Proceedings of the 2010 Workshop on Procedural Content Generation in Games

Article No.: 3, Pages 1 - 8

https://doi.org/10.1145/1814256.1814259

Published: 18 June 2010 Publication History

Abstract

A search-based procedural content generation (SBPCG) algorithm for strategy game maps is proposed. Two representations for strategy game maps are devised, along with a number of objectives relating to predicted player experience. A multiobjective evolutionary algorithm is used for searching the space of maps for candidates that satisfy pairs of these objectives. As the objectives are inherently partially conflicting, the algorithm generates Pareto fronts showing how these objectives can be balanced. Such fronts are argued to be a valuable tool for designers looking to balance various design needs. Choosing appropriate points (manually or automatically) on the Pareto fronts, maps can be found that exhibit good map design according to specified criteria, and could either be used directly in e.g. an RTS game or form the basis for further human design.

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Cited By

Önal EBulbul A(2025)A constructive approach to strategy game map generationEntertainment Computing10.1016/j.entcom.2024.10088652(100886)Online publication date: Jan-2025
https://doi.org/10.1016/j.entcom.2024.100886
Schaa HBarriga N(2024)Evaluating the Expressive Range of Super Mario Bros Level GeneratorsAlgorithms10.3390/a1707030717:7(307)Online publication date: 11-Jul-2024
https://doi.org/10.3390/a17070307
Nam SHsueh CRerkjirattikal PIkeda K(2024) Using Reinforcement Learning to Generate Levels of Super Mario Bros. With Quality and Diversity IEEE Transactions on Games10.1109/TG.2024.341647216:4(807-820)Online publication date: Dec-2024
https://doi.org/10.1109/TG.2024.3416472
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cover image ACM Other conferences

PCGames '10: Proceedings of the 2010 Workshop on Procedural Content Generation in Games

June 2010

67 pages

ISBN:9781450300230

DOI:10.1145/1814256

Program Chair:
Rafael Bidarra
Delft University of Technology

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SASDG: The Society for the Advancement of the Study of Digital Games

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2010

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Danish Agency for Science, Technology and Innovation

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FDG '10

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SASDG

FDG '10: International Conference on the Foundations of Digital Games

June 18, 2010

California, Monterey

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Overall Acceptance Rate 13 of 15 submissions, 87%

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Cited By

Önal EBulbul A(2025)A constructive approach to strategy game map generationEntertainment Computing10.1016/j.entcom.2024.10088652(100886)Online publication date: Jan-2025
https://doi.org/10.1016/j.entcom.2024.100886
Schaa HBarriga N(2024)Evaluating the Expressive Range of Super Mario Bros Level GeneratorsAlgorithms10.3390/a1707030717:7(307)Online publication date: 11-Jul-2024
https://doi.org/10.3390/a17070307
Nam SHsueh CRerkjirattikal PIkeda K(2024) Using Reinforcement Learning to Generate Levels of Super Mario Bros. With Quality and Diversity IEEE Transactions on Games10.1109/TG.2024.341647216:4(807-820)Online publication date: Dec-2024
https://doi.org/10.1109/TG.2024.3416472
Liang JLin HYue CYu KGuo YQiao K(2023)Multiobjective Differential Evolution With Speciation for Constrained Multimodal Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319425327:4(1115-1129)Online publication date: Aug-2023
https://doi.org/10.1109/TEVC.2022.3194253
Yannakakis GMelhart D(2023)Affective Game Computing: A SurveyProceedings of the IEEE10.1109/JPROC.2023.3315689111:10(1423-1444)Online publication date: Oct-2023
https://doi.org/10.1109/JPROC.2023.3315689
Cooper SGuzdial M(2023)path2level: Constraint-Based Level Generation from Paths2023 IEEE Conference on Games (CoG)10.1109/CoG57401.2023.10333205(1-4)Online publication date: 21-Aug-2023
https://doi.org/10.1109/CoG57401.2023.10333205
de Oliveira da Rocha Franco Ada Silva JMaia Jde Castro M(2023)Harnessing generative grammars and genetic algorithms for immersive 2D mapsEntertainment Computing10.1016/j.entcom.2023.10059547(100595)Online publication date: Aug-2023
https://doi.org/10.1016/j.entcom.2023.100595
Deng WZhang XZhou YLiu YZhou XChen HZhao H(2022)An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problemsInformation Sciences: an International Journal10.1016/j.ins.2021.11.052585:C(441-453)Online publication date: 1-Mar-2022
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Zhang JChu XYang FQu JWang S(2022)Multimodal and multi-objective optimization algorithm based on two-stage search frameworkApplied Intelligence10.1007/s10489-021-02969-052:11(12470-12496)Online publication date: 7-Feb-2022
https://doi.org/10.1007/s10489-021-02969-0
Gregor CAshlock DRuz GMacKinnon DKribs D(2022)A novel linear representation for evolving matricesSoft Computing10.1007/s00500-022-07043-626:14(6645-6657)Online publication date: 12-Apr-2022
https://doi.org/10.1007/s00500-022-07043-6
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