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Identifying Latent Semantics in Action Games for Player Modeling
Abstract
Machine learning approaches to player modeling traditionally employ a high-level game-knowledge-based feature for representing game sessions, and often player behavioral features as well. The present work makes use of generic low-level features and latent semantic analysis for unsupervised player modeling, but mostly for revealing underlying hidden information regarding game semantics that is not easily detectable beforehand.
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