Design of a Fuzzy Affective Agent Based on Typicality Degrees of Physiological Signals
Physiology-based emotionally intelligent paradigms provide an opportunity to enhance human computer interactions by continuously evoking and adapting to the user experiences in real-time. However, there are unresolved questions on how to model real-time emotionally intelligent applications through mapping of physiological patterns to users’ affective states.
In this study, we consider an approach for design of fuzzy affective agent based on the concept of typicality. We propose the use of typicality degrees of physiological patterns to construct the fuzzy rules representing the continuous transitions of user’s affective states. The approach was tested on experimental data in which physiological measures were recorded on players involved in an action game to characterize various gaming experiences. We show that, in addition to exploitation of the results to characterize users’ affective states through typicality degrees, this approach is a systematic way to automatically define fuzzy rules from experimental data for an affective agent to be used in real-time continuous assessment of user’s affective states.
KeywordsMachine learning fuzzy logic prototypes typicality degrees affective computing physiological signals
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- 1.Bouchon-Meunier, B.: Aggregation and Fusion of Imperfect Information. Physica-Verlag, Spring-Verlag Company (1998)Google Scholar
- 5.Csikszentmihalyi, M.: Harper and row flow: the psychology of optimal experience. Harper & Row, New York (1990)Google Scholar
- 6.Detyniecki, M.: Mathematical aggregation operators and their application to video querying. PhD thesis, Université Pierre et Marie Curie, France (2001)Google Scholar
- 10.Lesot, M.-J., Rifqi, M., Bouchon-Meunier, B.: Fuzzy prototypes: from a cognitive view to a machine learning principle. In: Bustince, H., Herrera, F., Montero, J. (eds.) Fuzzy sets and Their Extensions: Representation, Aggregation and Models. STUDFUZZ, vol. 220, pp. 431–452. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 11.Levillain, F., Orero, J.O., Rifqi, M., Bouchon-Meunier, B.: Characterizing player’s experience from physiological signals using fuzzy decision trees. In: IEEE Conference on Computational Intelligence and Games (2010)Google Scholar
- 15.Orero, J.O., Levillain, F., Damez-Fontaine, M., Rifqi, M., Bouchon-Meunier, B.: Assessing gameplay emotions from physiological signals: a fuzzy decision trees based model. In: International Conference on Kansei Engineering and Emotion Research (2010)Google Scholar
- 16.Picard, R.W.: Affective computing. The MIT Press, Cambridge (1997)Google Scholar
- 20.Rifqi, M.: Constructing prototypes from large databases. In: International Confrence on Information Processing and Management of Uncertainity in Knowledge-Based System, IPMU (1996)Google Scholar
- 22.Wagner, J., Kim, J., André, E.: From physiological signals to emotions: implementing and comparing selected methods for feature extraction and classification. In: IEEE International Conference in Multimedia and Expo, pp. 940–943 (2005)Google Scholar