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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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IEEE
01.12.2017
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| ISSN: | 1943-068X, 1943-0698 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Snodgrass, Sam Ontanon, Santiago |
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| Cites_doi | 10.1109/TASSP.1987.1165125 10.1145/1536513.1536548 10.2307/1575226 10.1109/ITW.2010.5593333 10.1109/5.18626 10.1007/978-1-4614-6312-2_6 10.1109/TCIAIG.2011.2166267 10.1214/009053606000000588 10.1016/j.eswa.2008.01.039 10.1109/TCIAIG.2011.2148116 10.1145/1814256.1814260 10.1109/TPAMI.1983.4767341 10.1145/2676467.2676506 |
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| Title | Learning to Generate Video Game Maps Using Markov Models |
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