From affect, behavior, and cognition to personality: an integrated personal character model for individual-like intelligent artifacts

An individual-like intelligent artifact is a special kind of humanoid which resembles a human being in assimilating aspects of its real human counterpart’s cognition and neurological functions. Such an individual-like intelligent artifact could have a number of far-reaching applications, such as in...

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Veröffentlicht in:World wide web (Bussum) 2020-03, Vol.23 (2), p.1217-1239
Hauptverfasser: Guo, Ao, Ma, Jianhua, Tan, Shunxiang, Sun, Guanqun
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Sprache:eng
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Zusammenfassung:An individual-like intelligent artifact is a special kind of humanoid which resembles a human being in assimilating aspects of its real human counterpart’s cognition and neurological functions. Such an individual-like intelligent artifact could have a number of far-reaching applications, such as in creating a digital clone of an individual and bringing about forms of digital immortality. Although such intelligent artifacts have been created in various forms, such as physical robots or digital avatars, these creations are still far from modeling the inner cognitive and neurological mechanisms of an individual human. To imbue individual-like intelligent artifacts with the characteristics of individuals, we propose a Personal Character Model that consists of personality, the characteristics of affect, behavior, and cognition, and the relations between these characteristics. According to differential psychology and personality psychology, personality is the set of essential characteristics that make a person unique whereas characteristics in affect, behavior, and cognition explain a person’s stable and abstract personality in their diverse daily behavior. In addition, relations among these characteristics serve as a bridge from one characteristic to another. To illustrate the computing process of the personal character model, we first designed three experiments to collect physiological data and behavior data from twenty participants. Then we selected data features from the collected data using correlational analysis. Finally, we computed several representative characteristics from selected data features and represented the computed results.
ISSN:1386-145X
1573-1413
DOI:10.1007/s11280-019-00713-w