This work presents the integration of Machine Learning techniques in the beta testing phase of Heartbot - Escape, a commercial video game, in order to help the game designer in the identification of the game parameters and player characteristics that have the most influence on metrics such as entertainment and difficulty. The game was analysed to define the actual input features and several classification outputs, data collection was designed and performed and data were then analysed
Machine Learning and Feature Selection for Videogame Beta Testing
Fabris, Lorenzo
2015/2016
Abstract
This work presents the integration of Machine Learning techniques in the beta testing phase of Heartbot - Escape, a commercial video game, in order to help the game designer in the identification of the game parameters and player characteristics that have the most influence on metrics such as entertainment and difficulty. The game was analysed to define the actual input features and several classification outputs, data collection was designed and performed and data were then analysedFile in questo prodotto:
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Utilizza questo identificativo per citare o creare un link a questo documento:
https://hdl.handle.net/20.500.12608/19607