Orion, A Generic Model for Data Mining: Application to Video Games

Julien Soler 1
1 Lab-STICC_ENIB_CID_IHSEV
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : The video game industry's needs are constantly changing. In the field of artificial intelligence, we identify in chapter 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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  • HAL Id : tel-01201960, version 1

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Julien Soler. Orion, A Generic Model for Data Mining: Application to Video Games. Artificial Intelligence [cs.AI]. UBO, 2015. English. ⟨tel-01201960⟩

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