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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Vydáno v:IEEE transactions on computational intelligence and AI in games. Ročník 9; číslo 4; s. 410 - 422
Hlavní autoři: Snodgrass, Sam, Ontanon, Santiago
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.12.2017
Témata:
<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
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Popis
Shrnutí: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 approaches are a promising avenue for such generators and, more specifically, map generators. In this paper, we explore Markov models as a means of modeling and generating content for multiple domains. We apply our Markov models to Super Mario Bros., Loderunner , and Kid Icarus in order to determine how well our models perform in terms of the playability of the content generated, the expressive ranges of the models, and the effects of training data on those expressive ranges.
ISSN:1943-068X
1943-0698
DOI:10.1109/TCIAIG.2016.2623560