research-article

Procedural Game Map Generation using Multi-leveled Cellular Automata by Machine learning

Authors:

Zhixuan Wu,

Yuwei Mao,

Qiyu LiAuthors Info & Claims

ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

Pages 168 - 172

https://doi.org/10.1145/3500931.3500962

Published: 22 December 2021 Publication History

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Abstract

The concept of Procedural Content Generation (PCG) has been intensively applied in the game industry for its capability of producing infinite game maps without any human effort. Innumerable games, such as Minecraft, Terraria, and No Man's Sky, successfully employed this technique to create unpredictable yet playful gaming experiences. While randomness is essential to adding engaging elements to a game, complete randomness may hurt the outcome of map generations by making a chaotic scene. To address this issue, this paper introduces an effective way of "tweaking" the randomness to generate flexible, endless, natural-looking game maps by machine learning.

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Cook, M. (2013) Generate random cave levels using cellular automata. https://gamedevelopment.tutsplus.com/tutorials/generate-random-cave-levels-using-cellular-automata--gamedev-9664.

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Cited By

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Beukman MFokam MKruger MAxelrod GNasir MIngram BRosman BJames S(2024)Hierarchically Composing Level Generators for the Creation of Complex StructuresIEEE Transactions on Games10.1109/TG.2023.329761916:2(459-469)Online publication date: Jun-2024
https://doi.org/10.1109/TG.2023.3297619
Pu JDuan HZhao JLong Y(2024)Rules for Expectation: Learning to Generate Rules via Social Environment ModelingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.333452634:8(6874-6887)Online publication date: Aug-2024
https://doi.org/10.1109/TCSVT.2023.3334526

Index Terms

Procedural Game Map Generation using Multi-leveled Cellular Automata by Machine learning
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics

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Published In

ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 2021

593 pages

ISBN:9781450395588

DOI:10.1145/3500931

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2021

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ISAIMS 2021

ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 29 - 31, 2021

Beijing, China

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Overall Acceptance Rate 53 of 112 submissions, 47%

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Cited By

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Beukman MFokam MKruger MAxelrod GNasir MIngram BRosman BJames S(2024)Hierarchically Composing Level Generators for the Creation of Complex StructuresIEEE Transactions on Games10.1109/TG.2023.329761916:2(459-469)Online publication date: Jun-2024
https://doi.org/10.1109/TG.2023.3297619
Pu JDuan HZhao JLong Y(2024)Rules for Expectation: Learning to Generate Rules via Social Environment ModelingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.333452634:8(6874-6887)Online publication date: Aug-2024
https://doi.org/10.1109/TCSVT.2023.3334526

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Dynamic Procedural Music Generation from NPC Attributes

Multi-dimensional player skill progression modelling for procedural content generation

Procedural Content Generation for Game Props? A Study on the Effects on User Experience