Design of a fuzzy affective agent based on typicality degrees of physiological signals
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Abstract
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.
Description
Conference paper presented at International Conference on Information Processing and Management in July 2014
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.
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.
Keywords
Machine learning, fuzzy logic, prototypes, typicality degrees, affective computing, physiological signals.