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| Author(s): |
Rosane Maria Maffei Vallim
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2013-07-11 |
| Examining board members: |
André Carlos Ponce de Leon Ferreira de Carvalho;
João Manuel Portela da Gama;
Estevam Rafael Hruschka Júnior;
Ivan Nunes da Silva;
Bianca Zadrozny
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| Advisor: | André Carlos Ponce de Leon Ferreira de Carvalho; João Manuel Portela da Gama |
| Abstract | |
One of the challenges of Artificial Intelligence applied to games is behavior learning, where the objective is to use statistics derived from the interaction between the player and the game environment in order to recognize particular player characteristics or to monitor the evolution of a players behavior along time. The majority of work developed in this area applies models that were previously learned through the use of Machine Learning techniques. However, only a few pieces of work consider that the players behavior can evolve over time and, therefore, recognizing when behavior changes happen is the first step towards the production of games that adapt to the players needs. In order to detect changes in the behavior of a player, incremental algorithms are necessary, what motivates the study of change detection algorithms from the area of Data Stream Mining. However, some of the characteristics of the algorithms available in the literature make their application to the task of change detection in games unfeasible. To overcome these difficulties, this work proposes two new approaches for change detection. The first approach is based on an incremental clustering and novelty detection algorithm which is independent of the number and format of clusters and uses a mechanism for change detection based on sliding windows. The second approach, on the other hand, is based on the comparison of consecutive time windows using spectrograms created from the data inside each window. Experimental results using simulations and data from commercial games indicate the applicability of the proposed algorithms in the task of detecting a players changing behavior, as well as present their advantage when compared to other change detection algorithms available in the literature (AU) | |
| FAPESP's process: | 10/11250-0 - Mining Data Streams for Computer Games |
| Grantee: | Rosane Maria Maffei Vallim |
| Support Opportunities: | Scholarships in Brazil - Doctorate |