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| − | * Andrea Tommaso Bonanno | + | * [[User:AndreaTommasoBonanno|Andrea Tommaso Bonanno]] [[ftp://ftp.elet.polimi.it/outgoing/Andrea.Bonarini/share/TesiBonanno.pdf Tesi]] |
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| − | 13/3-27/3 Reading "Reinforcement Learning: an introduction"
| + | '''FILMATI''' |
| − | | + | [http://dl.dropbox.com/u/2083950/video/16.avi Video 16] |
| − | 28/3-23/4 Reading several publications on ANS specificity and physiological signal analysis. Studying 3 different games(TORCS, Frets on Fire, Beaver Valley) for the identification of useful parameters for the purpose of the project.
| + | [http://dl.dropbox.com/u/2083950/video/17.avi Video 17] |
| − | | + | [http://dl.dropbox.com/u/2083950/video/31.avi Video 31] |
| − | 24/4-30/4 Working on the developmnent of a XML parser capable of extracting labels for classifying the difficulty of tracks of TORCS. Work prior to first acquisitions.
| + | [http://dl.dropbox.com/u/2083950/video/55.avi Video 55] |
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| − | 01/05-31/05 Working on first acquisitions of biological signals from myself (and Andrea Campana) while experiencing the videogame TORCS. In the meanwhile a first software prototype has been developed aiming at discovering specific patterns in players' emotions while playing the game. In this stage I started from the work already done from the Affective Videogames research group of DEI.
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| − | First results on the single user show some specific pattern in user's emotions. Next stage will be devoted to the acquisition of biological data from many other players to study the inter-subject variability and to find common invariants.
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| − | 01/06-15/06 Acquisition of biological data from many different subjects who played TORCS as volunteers according to a planned set of experiments.
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| − | 16/06-31/08 Bibliographic research and reading literature on Affective Videogames (approximately 80 scientific publications); looking for modifiable parameters in TORCS's code which can change user's responsiveness to the game. Identification of those parameters and coding.
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| − | 01/09-30/09 Starting to look at the code about the Online Classifier (MATLAB) and studying Bayesian Network as a useful technique for modeling the system.
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Latest revision as of 18:33, 3 November 2010
FILMATI
Video 16
Video 17
Video 31
Video 55
