Journal article
Learning to Generate Video Game Maps Using Markov Models
IEEE transactions on computational intelligence and AI in games, v 9(4), pp 410-422
01 Dec 2017
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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Details
- Title
- Learning to Generate Video Game Maps Using Markov Models
- Creators
- Sam Snodgrass - Drexel UniversitySantiago Ontanon - Drexel University
- Publication Details
- IEEE transactions on computational intelligence and AI in games, v 9(4), pp 410-422
- Publisher
- IEEE
- Number of pages
- 13
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000418422900009
- Scopus ID
- 2-s2.0-85044400355
- Other Identifier
- 991019167983504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Software Engineering