학술논문

Preliminary personality model for social robots based on the Cognitive-Affective Processing System theory
Document Type
Conference
Source
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 2022 IEEE International Conference on. :223-228 Oct, 2022
Subject
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Extended reality
Social robots
Psychology
Neural engineering
Focusing
Metrology
User experience
Social Robotics
Robot Personality
Cognitive-Affective Processing System Theory
Expert Systems
Language
Abstract
Personality influences human behaviour during social interactions as well as is influenced by the situation in which these interactions take place. To date, the reciprocal interplay between personality and the situation is still little investigated in modelling personality for social robots, even if the personality psychology field has widely recognised this interplay. Therefore, in this study, we propose a novel personality model for social robots based on the formalisation of the Cognitive-Affective Processing System theory of personality. According to this theory, we conceived a personality model as a system that interacts with features of the situation to generate the behaviour. We focus on a bottom-up approach by detailing the core components of the system and by posing particular emphasis on its link with induced emotions. Furthermore, we preliminary implemented the proposed personality model taking advantage of the flexibility offered by expert systems and endowing the personality model with some degree of explainability.Our work approaches social robot personality focusing on increasing the human-likeness of the model, rather than improving the imitation of human behaviours, to improve the believability and the user experience of social human-robot interactions.