The Learning of Zelda: Data-Driven Learning of Level Topology

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TitleThe Learning of Zelda: Data-Driven Learning of Level Topology
Publication TypeConference Paper
Year of Publication2015
AuthorsSummerville, AJames, Behrooz, M, Mateas, M, Jhala, A
Conference NameProceedings of the 10th International Conference on the Foundations of Digital Games
Date PublishedJune
KeywordsBayesian network, data-driven level design, games, level design, machine learning, probabilistic learning, procedural content generation
The majority of Procedural Content Generation (PCG) research has made use of human authored rules, heuristics and evaluation metrics. Machine learning techniques have gone relatively unused in PCG. We introduce a data-driven level generation approach, and apply it to the of dungeons for Zelda-like Action Roleplaying Games (ARPGs). We use Bayesian Networks (BNs) to learn distributional information about level topology. The learned networks can then be sampled to generate levels that have the same statistical properties as human authored levels.