Coevolution of AI and Level Generators for Super Mario Game
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440728" target="_blank" >RIV/00216208:11320/21:10440728 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CEC45853.2021.9504742" target="_blank" >https://doi.org/10.1109/CEC45853.2021.9504742</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC45853.2021.9504742" target="_blank" >10.1109/CEC45853.2021.9504742</a>
Alternative languages
Result language
angličtina
Original language name
Coevolution of AI and Level Generators for Super Mario Game
Original language description
Procedural content generation (PCG) is now used in many games to generate a wide variety of content. One way of evaluating this content is by artificial intelligence (AI) controlled players. Inversely, PCG content can also be used when training AI players to ensure generalization. Evolutionary algorithms are employed in both AI and PCG fields, but rarely simultaneously. In this work, we use evolutionary algorithms for both AI players and level generation in the platformer game Super Mario. We further combine them into a coevolution, where the AI players are evaluated by adapting level generators, and vice versa, level generators are evaluated by adapting AI players. This yields an AI player trained on gradually more difficult levels and a sequence of level generators with gradually increasing difficulty. Such sequence of generators might be useful for human game playing in commercial games.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 IEEE Congress on Evolutionary Computation (CEC 2021)
ISBN
978-1-72818-392-3
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
2093-2100
Publisher name
IEEE
Place of publication
New York
Event location
Online
Event date
Jun 28, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000703866100264