• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.185-209
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• 2023

With the advent of the 4th industrial revolution, various technologies have received much attention. Technologies related to the 4th industry include the Internet of Things (IoT), big data, artificial intelligence, virtual reality (VR), 3D printers, and robotics, and these technologies are often converged. In particular, the robotics field is combined with technologies such as big data, artificial intelligence, VR, and digital twins. Accordingly, much research using robotics is being conducted, which is applied to distribution, airports, hotels, restaurants, and transportation fields. In the given situation, research on human-robot interaction is attracting attention, but it has not yet reached the level of user satisfaction. However, research on robots capable of perfect communication is steadily being conducted, and it is expected that it will be able to replace human emotional labor. Therefore, it is necessary to discuss whether the current human-robot interaction technology can be applied to business. To this end, this study first examines the trend of human-robot interaction technology. Second, we compare LDA (Latent Dirichlet Allocation) topic modeling and BERTopic topic modeling methods. As a result, we found that the concept of human-robot interaction and basic interaction was discussed in the studies from 1992 to 2002. From 2003 to 2012, many studies on social expression were conducted, and studies related to judgment such as face detection and recognition were conducted. In the studies from 2013 to 2022, service topics such as elderly nursing, education, and autism treatment appeared, and research on social expression continued. However, it seems that it has not yet reached the level that can be applied to business. As a result of comparing LDA (Latent Dirichlet Allocation) topic modeling and the BERTopic topic modeling method, it was confirmed that BERTopic is a superior method to LDA.

• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.459-480
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• 2010

A dialogue system includes various morphological analyses for recognizing a user's intention from the user's utterances. However, a user can represent various intentions via emotional states in addition to morphological expressions. Thus, a user's emotion recognition can analyze a user's intention in various manners. This paper presents a new method to automatically recognize a user's emotion for a dialogue system. For general emotions, we define nine categories using a psychological approach. For an optimal feature set, we organize a combination of sentential, a priori, and context features. Then, we employ a support vector machine (SVM) that has been widely used in various learning tasks to automatically classify a user's emotions. The experiment results show that our method has a 62.8% F-measure, 15% higher than the reference system.

• Journal of Korean Society for Quality Management
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• v.52 no.1
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• pp.95-113
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• 2024

Purpose: This study focused on the societal changes associated with the entry into an ultra-aged society and the increase in single-person households. The core objective of this research is to investigate how social intelligent robots can bring about positive changes in the lives of individuals in single-person households and how such changes influence user satisfaction and the intention to use these robots. Methods: The study employed a cross-sectional analysis using a structural equation model. A survey designed to assess the impact of social intelligent robots' characteristics, such as perceived encouragement, empathy, presence, appearance, and attachment, on user satisfaction and usage intentions was conducted. Data were collected from a total of 335 users and analyzed using the structural equation model. Results: In the characteristics of social intelligent robots for single-person households, it was found that empathy, presence, and attachment significantly influenced satisfaction, while perceived encouragement, empathy, and attachment significantly influenced usage intentions. The research results indicate differences between enhancing user satisfaction and increasing the intention to use social intelligent robots. The findings suggest the essential need for a user-centric approach in the design and development of social intelligent robots. Additionally, it was observed that emotional support plays a crucial role in users' experiences with social intelligent robots. Conclusion: This study verified the impact of social intelligent robots on satisfaction and usage intentions based on users' experiences. It examined the influence of linguistic, visual, and personal characteristics of robots on user experiences, providing insights into how technological and human aspects of social intelligent robots interact to shape user satisfaction and usage intentions. Consequently, the study confirmed that social intelligent robots can bring positive changes to human life, emphasizing the necessity for the advancement of robot technology in a human-centric direction.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.33-40
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• 2010

The study is to examine relationship between the level of depression and facial EMG responses during the humor condition. Forty-three children(age range 22-49 years) participated in the study. The Korean Personality Inventory for Children(KPI-C) was used to measure the level of depression in children. While children were presented to audio-visual film clip inducing humor, facial EMG were measured on their faces(bilateral corrugators and orbicularis). A baseline state was measured during 60 seconds before the presentation of the stimulus, i.e., emotional state lasting 120 seconds. Participants were asked to report the intensity of their experienced emotion. The results of emotion assessment showed 95.3% appropriateness and 3.81 intensity on the 5 points Likert scale). Facial EMG showed a significant increase while participants experiencing humor compared to baseline state. Additionally, the result showed a negative correlation between right corrugator responses and the level of depression. The study findings showed the more children experienced depression, the less facial EMG activity they had while experiencing humor.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.70-83
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• 2021

The issue of predicting the relationship between humans and AI robots is a 'strong AI' problem. Many experts predict the tragic ending which is a strong AI with superior thinking ability than humans will conquer humans. Due to the expectations of AI robots are projected onto media, the 'morally good AI' that meets human expectations is an important issue. However, the demand for good AI and the realization of perfect technology is not limited to machines. Rather, it appears as a result of putting all responsibility on humans, driving humans into immoral beings and turning them into human and human problems, which is resulting in more alienation and discrimination. As such, the result of technology interacts with the human being used and its properties are determined and developed according to the reaction. This again affects humans. Therefore, AI technology that considers human emotions in consideration of interaction is also important. Therefore, this study will clarify the process that the demand for 'Good AI' in the relationship of AI to humans with Randall Collins' Interaction Ritual Chain. Emotional energy in Interaction Ritual Chain has explained the formation of human bonds. Also, the methodology is a type of thinking experiment and explained through Janet and surrounding characters in the TV show .