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Human-centered modeling for style-based adaptive games
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DOI：10.1631/jzus.A0820593.png)
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This letter proposes a categorization matrix to analyze the playing style of a computer game player for a shooting game genre. Our aim is to use human-centered modeling as a strategy for adaptive games based on entertainment measure to evaluate the playing experience. We utilized a self-organizing map (SOM) to cluster the player's style with the data obtained while playing the game. We further argued that style-based adaptation contributes to higher enjoyment, and this is reflected in our experiment using a supervised multilayered perceptron (MLP) network.
Keyword:
Player modeling
Categorization matrix
Adaptive games
Human-centered modeling
Data clustering
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