Coevolution of AI and Level Generators for Super Mario Game

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Coevolution of AI and Level Generators for Super Mario Game

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Coevolution of AI and Level Generators for Super Mario Game

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 IEEE Congress on Evolutionary Computation (CEC 2021)

ISBN

978-1-72818-392-3

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

2093-2100

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Online

Datum konání akce

28. 6. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000703866100264