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This paper explores the effect of task interval within a multi-conversational agent control system on the perceived workload and operational behaviors of administrators. An experimental study was conducted employing three distinctive task intervals, namely 10s, 30s, and 60s, with a sample of 39 participants. The results indicated a direct correlation between shorter task intervals and increased subjective workload of administrators, which correspondingly resulted in expedited response times. On the contrary, longer intervals were associated with enhanced response quality and more meticulous behavior. Eye-tracking data analysis further provided insights into user engagement; shorter intervals heightened focus on chat content, while longer intervals led to an increase in distractions. The findings underscore the significance of task intervals as a pivotal mechanism for optimization of multi-conversational agent control systems and enhancement of conversation quality. For future work, we plan to investigate strategies aimed at mitigating administrators' workload and bolstering user experiences within such systems.

This paper explores the effect of task interval within a multi-conversational agent control system on the perceived workload and operational behaviors of administrators. An experimental study was conducted employing three distinctive task intervals, namely 10s, 30s, and 60s, with a sample of 39 participants. The results indicated a direct correlation between shorter task intervals and increased subjective workload of administrators, which correspondingly resulted in expedited response times. On the contrary, longer intervals were associated with enhanced response quality and more meticulous behavior. Eye-tracking data analysis further provided insights into user engagement; shorter intervals heightened focus on chat content, while longer intervals led to an increase in distractions. The findings underscore the significance of task intervals as a pivotal mechanism for optimization of multi-conversational agent control systems and enhancement of conversation quality. For future work, we plan to investigate strategies aimed at mitigating administrators' workload and bolstering user experiences within such systems.

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기사명 저자명 페이지 원문 목차
대규모 언어 모델(LLM)의 사전 지식을 활용한 3차원 장면 그래프 생성 = 3D scene graph generation using prior knowledge from large language model (LLM) 백호준, 김인철 p. 859-873
LLM 기반 하이브리드 아바타 에이전트 시스템의 사용성 평가 = Evaluating the usability of an LLM-aided hybrid avatar agent system 금준영, 김태연, 이명호 p. 874-886
디지털 트윈 기반 합성 데이터 자동 생성 모델을 이용한 화재 조기 검출 시스템 = Early fire detection system by synthetic dataset automatic generation model based on digital twin 김현철, 이석환, 옥수열 p. 887-897
딥러닝 기반의 객체 탐지 모델을 활용한 어종 및 어병 탐지 시스템 = Fish species and disease detection system using deep learning-based object detection model 배명훈, 박준, 정세훈, 심춘보 p. 898-910
미세먼지 농도 예측을 위한 어텐션 기반 합성곱 장단기 메모리 모델 = Attention based-convLSTM-DNN networks for fine dust concentration prediction 이준민, 김경태, 최재영 p. 911-924
초소형 객체 검출을 위한 교차 전이 학습에 기반한 딥러닝 네트워크 알고리즘 = Deep learning network algorithm based on X-transfer learning for micro object detection 권오설 p. 925-931
DAG 블록체인 기반의 향상된 CCTV 함체기록 관리모델 = Enhanced CCTV enclosure record management model through DAG blockchain 유관우, 이병문 p. 932-943
결측치 보간 알고리즘을 적용한 앙상블 기반의 태양광 발전량 예측 시스템 = Ensemble-based solar power prediction system using missing value interpolation algorithm 박수빈, 김진성, 정세훈, 심춘보 p. 944-955
자기공명 호환성 향상을 위한 롬버스 메커니즘 기반의 이식형 골전도 압전트랜스듀서 설계 = Design of bone conduction implants piezoelectric transducer based on rhombus mechanism for magnetic resonance compatibility improvement 신동호, 임형규, 김명남, 성기웅 p. 956-964
3차원 건축물 모델링 자동화를 위한 딥러닝 기반 벽 구조 객체 추출 방법 = Deep learning based wall structure object extraction for 3D building modeling automation 유형준, 이경로, 류제호, 이승주, 이종훈 p. 956-973
추천시스템을 위한 효율적인 Low-Rank 행렬완성 업데이트 알고리즘 = Efficient low-rank matrix completion updating algorithm for recommender system 이근섭 p. 974-981
다중 대화형 에이전트 제어 시스템에서 작업 간격이 작업 부하 및 운용행동에 미치는 영향 분석 = Examining the impacts of task interval on subjective workload and operational behavior in a multi-conversational agent control system 김규형, 문성호, 이명호 p. 982-994
다중 파라미터 MR 영상에서 유사 의료 영상으로 미세 조정한 자기지도학습 모델을 사용한 전립선암 악성도 예측 성능 개선 = Improvement of prostate cancer aggressiveness prediction performance using a self-supervised learning model fine-turned on similar medical images from multi-parametric MR images 신예진, 이민진, 홍헬렌, 황성일 p. 995-1002
딥러닝을 이용한 재무와 비재무 정보 기반 기업부도 예측 분석에 관한 연구 = Research on corporate bankruptcy prediction analysis based on financial and non-financial information using deep learning 박중현 p. 1003-1012
사용자 의사결정을 고려한 XAI(eXplainable Artificial intelligence) 기반의 피싱 사이트 탐지 모델 = Phishing website detection model for user decision making based on XAI 김대엽 p. 1013-1026
Deep learning architecture for choice-based recommendation system : a case study of flight search engine Ahmed Hamdi Abdurhman, Jihwan Lee, Donghyun Kim, ByeongSeok Yu p. 1027-1041
DNN과 계층 연관성 전파를 이용한 PM2.5 고농도 사례의 인자 중요도 분석 = Analysis of factor importance of PM2.5 high concentration case using DNN and layer-wise relevance propagation 유숙현 p. 1042-1054
청각장애인의 메타버스 콘텐츠 감상을 위한 보조 기능의 사용성 분석 = An analysis of usability on secondary functions for the hearing impaired to enjoy metaverse contents : focusing on popular music concerts : 대중음악 콘서트를 중심으로 김남희, 임순범 p. 1064-1074
GAN을 이용한 웹툰 배경 이미지의 생성과 분석 = Generation and analysis of webtoon background images using GAN 이제경, 김정기, 안정인, 임지연, 차경애 p. 1075-1085
디자인 사고의 주요활동 및 핵심요소에 관한 연구 = A study on the main activities and core elements of design thinking 류안영, 손원준 p. 1086-1096
다국적 각색 영화가 관객 만족도에 미치는 영향 연구 = A study on the influence of audience satisfaction on multinational adapted films : adapted movie <Heosamgwan> focusing on <Chronicle of a Blood Merchant> : <허삼관매혈기>를 각색한 영화 <허삼관>을 중심으로 유원원, 이연우, 김치용 p. 1055-1063

참고문헌 (27건) : 자료제공( 네이버학술정보 )

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번호 참고문헌 국회도서관 소장유무
1 1 ] C.B. Frey and M.A. Osborne, “The Future of Employment: How Susceptible are Jobs to Computerisation?,” Technological Forecasting and Social Change, Vol. 114, pp. 254-280, 2017. 미소장
2 2 ] J.C.R. Licklider, “Man-Computer Symbiosis,”IRE Transactions on Human Factors in Electronics, Vol. 1, No. 1, pp. 4-11, 1960. 미소장
3 3 ] J. Kum, K. Kim, and M. Lee, “The Hybrid Avatar-Agent System,” Academic Conference of the Korean Computer Graphics Society, pp. 97-98, 2022. 미소장
4 4 ] T. Kawahara, N. Muramatsu, K. Yamamoto, D. Lala, and K. Inoue, “Semi-Autonomous Avatar Enabling Unconstrained Parallel Conversations–Seamless Hybrid of WOZ and Autonomous Dialogue Systems–,” Advanced Robotics, Vol. 35, No. 11, pp. 657-663, 2021. 미소장
5 5 ] J. Devlin, M.W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding,”arXiv Preprint, arXiv:1810. 04805, 2018. 미소장
6 6 ] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, et al., “RoBERTa: A Robustly Optimized BERT Pretraining Approach,” arXiv Preprint, arXiv:1907.11692, 2019. 미소장
7 7 ] Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, R. Soricut, et al., “Albert: A Lite Bert for Self-Supervised Learning of Language Representations,” arXiv Preprint, arXiv:1909. 11942, 2019. 미소장
8 8 ] K. Clark, M.T. Luong, Q.V. Le, and C.D. Manning, “Electra: Pre-Training Text Encoders as Discriminators Rather than Generators,”arXiv Preprint, arXiv:2003.10555, 2020. 미소장
9 9 ] M. Joshi, D. Chen, Y. Liu, D.S. Weld, L. Zettlemoyer, O. Levy, et al., “Spanbert: Improving Pre-training by Representing and Predicting Spans,” Transactions of the Association for Computational Linguistics, Vol. 8, pp. 64-77, 2020. 미소장
10 OpenAI, “GPT-4 Technical Report,” arXiv Preprint, arXiv:2303.08774, 2023. 미소장
11 B. Kim, H. Kim, S.W. Lee, G. Lee, D. Kwak, and D.H. Jeon, et al., “What Changes can Large-scale Language Models Bring? Intensive Study on Hyperclova: Billions-Scale Korean Generative Pretrained Transformers,” arXiv Preprint, arXiv:2109.04650, 2021. 미소장
12 R. Higashinaka, K. Funakoshi, M. Araki, H. Tsukahara, Y. Kobayashi, M. Mizukami, et al., “Towards Taxonomy of Errors in Chat-Oriented Dialogue Systems,” Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 87-95, 2015. 미소장
13 R. Higashinaka, K. Funakoshi, M. Mizukami, H. Tsukahara, Y. Kobayashi, M. Araki, et al., “Analyzing Dialogue Breakdowns in Chat-Oriented Dialogue Systems,” Proceedings of the Interspeech Satelite Workshop, Errors by Humans and Machines in Multimedia, Multimodal and Multilingual Data Processing, pp. 11-13, 2015. 미소장
14 R. Higashinaka, L.F. D’Haro, B.A. Shawar, R.E. Banchs, K. Funakoshi, M. Inaba, et al., “Overview of the Dialogue Breakdown Detection Challenge 4,” Increasing Naturalness and Flexibility in Spoken Dialogue Interaction:10th International Workshop on Spoken Dialogue Systems, Springer Singapore, pp. 403-417, 2021. 미소장
15 D.F. Glas, T. Kanda, H. Ishiguro, and N. Hagita, “Simultaneous Teleoperation of Multiple Social Robots,” Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction, pp. 311-318, 2008. 미소장
16 S. Kishore, X.N. Muncunill1, P. Bourdin, K. Or-Berkers, D. Friedman, M. Slater, et al., “Multi-Destination Beaming: Apparently Being in Three Places at Once through Robotic and Virtual Embodiment,” Frontiers in Robotics and AI , Vol. 3, pp. 1-13, 2016. 미소장
17 R. Miura, S. Kasahara, M. Kitazaki, A. Ver-hulst, M. Inami, M. Sugimoto, et al., “Multi Soma: Motor and Gaze Analysis on Distributed Embodiment with Synchronized Behavior and Perception,” Frontiers in Computer Science, Vol. 4, pp. 1-15, 2022. 미소장
18 M. Huang, X. Zhu, and J. Gao, “Challenges in Building Intelligent Open-domain Dialog Systems,”ACM Transactions on Information Systems (TOIS), Vol. 38, No. 3, pp. 1-32, 2020. 미소장
19 D. Kang, W. Ammar, B. Dalvi, M.V. Zuylen, S. Kohlmeier, E. Hovy, et al., “A Dataset of Peer Reviews: Collection, Insights and NLP Applications,” arXiv Preprint, arXiv:1804. 09635, 2018. 미소장
20 N. Yee, J.N. Bailenson, and K. Rickertsen, “A Meta-Analysis of the Impact of the Inclusion and Realism of Human-Like Faces on User Experiences in Interfaces,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1-10, 2007. 미소장
21 S.G. Hart, “NASA-Task Load Index (NASATLX):20 Years Later,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 50, No. 9, pp. 904-908, 2006. 미소장
22 K.L. Nowak and F. Biocca, “The Effect of the Agency and Anthropomorphism on Users'Sense of Telepresence, Copresence, and Social Presence in Virtual Environments,” Presence:Teleoperators & Virtual Environments, Vol. 12, No 5, pp. 481-494, 2003. 미소장
23 J.N. Bailenson, J. Blascovich, A.C. Beall, and J. Loomis, “Equilibrium Theory Revisited:Mutual Gaze and Personal Space in Virtual Environments,” Presence: Teleoperators &Virtual Environments, Vol. 10, No 6, pp. 583-598, 2001. 미소장
24 D. Adiwardana, M. Luong, D.R. So, J. Hall, N. Fiedel, R. Thoppilan, et al, “Towards a Human-Like Open-Domain Chatbot," arXiv Preprint, arXiv:2001.09977, 2020. 미소장
25 J.Y. Chen and M.J. Barnes, “Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues,” IEEE Transactions on Human-Machine Systems, Vol. 44, No 1, pp. 13-29, 2014. 미소장
26 K. Zheng, D.F. Glas, T. Kanda, H. Ishiguro, and N, Hagita, “How Many Social Robots can One Operator Control?,” Proceedings of the 6th International Conference on Human-Robot Interaction, pp. 379-386, 2011. 미소장
27 M.K. Hwang, S.H. Lee, C.Y. Kim, and M.W. Kwon, “A Study on the Visual Attention of Banner Advertising on Portal Site Using Eye Tracking,” Journal of Korea Multimedia Society, Vol. 26, No 1, pp. 46-54, 2023. 미소장

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