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Orion, un modèle générique pour la fouille de données : application aux jeux vidéo

Abstract : The video game industry's needs are constantly changing. In the field of artificial intelligence, we identify inchapter 1, the different needs of industry in this area. We believe that the design of a learning behavior through imitation solution that is functional and efficient would cover most of these needs. In chapter 2, we show that data mining techniques can be very useful to provide such a solution. However, for now, these techniques are not sufficient to automatically build a comprehensive behavior that would be usable in modern video games. In chapter 3, we propose a generic model to learn behavior by imitating human players: Orion.This model consists of two parts, a structural model and a behavioral model. The structural model provides a general data mining framework, providing an abstraction of the different methods used in this research. This framework allows us to build a general purpose tool with better possibilities for visualizing than existing data mining tools. The behavioral model is designed to integrate data mining techniques in a more general architecture and is based on the Behavior Trees. In chapter 4, we illustrate how we use our model by implementing the behavior of players in the Pong and Unreal Tournament 3 games using Orion. In chapter 5,we identify possible improvements, both of our data mining framework and our behavioral model.
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Submitted on : Monday, July 1, 2019 - 12:34:13 PM
Last modification on : Wednesday, September 16, 2020 - 9:56:58 AM


Version validated by the jury (STAR)


  • HAL Id : tel-02169586, version 1


Julien Soler. Orion, un modèle générique pour la fouille de données : application aux jeux vidéo. Intelligence artificielle [cs.AI]. Université de Bretagne occidentale - Brest, 2015. Français. ⟨NNT : 2015BRES0035⟩. ⟨tel-02169586⟩



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