Learning to Generate Video Game Maps Using Markov Models

Procedural content generation has become a popular research topic in recent years. However, most content generation systems are specialized to a single game. We are interested in methods that can generate content for a wide variety of games without a game-specific algorithm design. Statistical appro...

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Veröffentlicht in:	IEEE transactions on computational intelligence and AI in games. Jg. 9; H. 4; S. 410 - 422
Hauptverfasser:	Snodgrass, Sam, Ontanon, Santiago
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2017
Schlagworte:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Loderunner <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Super Mario Bros Algorithm design and analysis Computational modeling Data models Games Markov models Markov processes procedural content generation Training Training data
ISSN:	1943-068X, 1943-0698
Online-Zugang:	Volltext
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