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Journal articles on the topic 'Human machine interaction'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 October 2024

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1

Prepin, Ken, and Arnaud Revel. "Human–machine interaction as a model of machine–machine interaction: how to make machines interact as humans do." Advanced Robotics 21, no. 15 (January 2007): 1709–23. http://dx.doi.org/10.1163/156855307782506192.

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2

Imamguluyev, Rahib, Tunzala Imanova, Parvana Hasanova, Ulviyya Poladova, Jamala Nabiyeva, Arzu Mammadova, Sevda Hajizada, and Hagigat Amrullayev. "Revolutionizing Human-Machine Interaction: Fuzzy Logic in interface Design." International Journal of Research Publication and Reviews 5, no. 8 (August 2024): 2939–49. http://dx.doi.org/10.55248/gengpi.5.0824.2155.

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3

Hoc, Jean-Michel. "From human – machine interaction to human – machine cooperation." Ergonomics 43, no. 7 (July 2000): 833–43. http://dx.doi.org/10.1080/001401300409044.

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4

Liu, Conghui. "Human-Machine Trust Interaction." International Journal of Dependable and Trustworthy Information Systems 1, no. 4 (October 2010): 61–74. http://dx.doi.org/10.4018/jdtis.2010100104.

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Improving user’s trust appropriately could help in designing an intelligent system and make it work effectively, especially with the fast growth of Web-base technology. This chapter introduces the solutions of improving user’s trust in human-machine interaction (HMI), especially for electronic commerce (e-commerce). The author firstly reviews the concept of trust and the main factors that affects the appropriateness of user’s trust in human-machine interaction, such as the properties of machine systems, the properties of human, and context. On the basis of these, the author further discusses the current state, challenges, problems and limitations of establishing and improving the user’s trust in human-machine interaction. Finally, the author summarizes and evaluates the existing solutions for improving the user’s trust appropriately in e-commerce environment.

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5

Meyer, Joachim, Chris Miller, Peter Hancock, Ewart J. de Visser, and Michael Dorneich. "Politeness in Machine-Human and Human-Human Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 279–83. http://dx.doi.org/10.1177/1541931213601064.

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Computers communicate with humans in ways that increasingly resemble interactions between humans. Nuances in expression and responses to human behavior become more sophisticated, and they approach those of human-human interaction. The question arises whether we want systems eventually to behave like humans, or whether systems should, even when much more developed, still adhere to rules that are different from the rules governing interpersonal communication. The panel addresses this issue from various perspectives, eventually aiming to gain some insights into the question of the direction to which the development of machine-human communication and the etiquette implemented in the systems should move.

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6

King, Raymond E. "Handbook of human-machine interaction." Aviation, Space, and Environmental Medicine 83, no. 8 (August 1, 2012): 811. http://dx.doi.org/10.3357/asem.3232.2012.

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7

Okura, Michiko. "Interface for Human-machine Interaction." TRENDS IN THE SCIENCES 10, no. 8 (2005): 52–55. http://dx.doi.org/10.5363/tits.10.8_52.

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8

Gouvrit, Florence. "Empathy and Human-Machine Interaction." International Journal of Synthetic Emotions 4, no. 2 (July 2013): 8–21. http://dx.doi.org/10.4018/ijse.2013070102.

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This paper presents the framework of the author’s practice and research exploring empathy and human-machine interaction in projects involving robotic art and video installations and performance. The works investigate emotions and embodiment, presence and absence, relationships and loss, and ways to implicate these ideas in encounters between technology-based artwork and the viewer.

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9

NEWELL, CHRISTOPHER, ALISTAIR D. N. EDWARDS, and PAUL CAIRNS. "‘Liveness’ in human-machine interaction." International Journal of Performance Arts and Digital Media 7, no. 2 (October 4, 2011): 221–37. http://dx.doi.org/10.1386/padm.7.2.221_1.

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Haqqu, Rizca, and Salwa Nur Rohmah. "Interaction Process Between Humans and ChatGPT in the Context of Interpersonal Communication." Jurnal Ilmiah LISKI (Lingkar Studi Komunikasi) 10, no. 1 (April 5, 2024): 23. http://dx.doi.org/10.25124/liski.v10i1.7216.

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This study examines human interaction with artificial intelligence technology, focusing on the implementation of ChatGPT, a chatbot developed by OpenAI. Through the Human-Machine Communication (HMC) approach, the research describes human-like attributes in ChatGPT, exploring emotional responses and utility in educational, professional, and personal contexts. Qualitative research methods with triangulation techniques were used for a holistic understanding, involving interviews, observations, and document analysis. The results indicate that ChatGPT can provide adaptive responses, adjusting language style, and presenting information with self-awareness. Comparisons between human-to-human and human-to-machine interactions, particularly through ChatGPT, reveal significant differences. In human-to-human communication, the primary role is given to humans as message sources, while in machine communication, ChatGPT becomes an interactive partner, especially in text messages. Despite similarities in interpersonal communication features, such as feedback, personal relationship cues are more pronounced in human-to-human interactions. Factors like self-concept, openness, and confidence are dominant in human-to-human communication, while AI literacy becomes crucial in interactions with machines. Keywords: Human-Machine Communication, ChatGPT, Interaction, Interpersonal

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11

Animesh, Kumar, and Dr Srikanth V. "Enhancing Healthcare through Human-Robot Interaction using AI and Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (March 21, 2024): 184–90. http://dx.doi.org/10.55248/gengpi.5.0324.0831.

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12

V., Dr Suma. "COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW." Journal of Trends in Computer Science and Smart Technology 2019, no. 02 (December 29, 2019): 131–39. http://dx.doi.org/10.36548/jtcsst.2019.2.006.

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The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and neural networks. The paper presents the enhanced capabilities of the computer vision experienced in various applications related to the interactions between the human and machines involving the artificial intelligence, deep learning and the neural networks.

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13

Komarova, V., J. Lonska, V. Tumalavičius, and A. Krasko. "Artificial sociality in the human-machine interaction." RUDN Journal of Sociology 21, no. 2 (December 15, 2021): 377–90. http://dx.doi.org/10.22363/2313-2272-2021-21-2-377-390.

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The article aims at clarifying the concept artificial sociality in the human-machine interaction by answering the question whether artificial sociality is a prerequisite or a result of this interaction. The authors conducted a logical analysis of the definitions of sociality and artificial sociality as presented in the scientific literature, and conducted an empirical study of artificial sociality in the human-machine interaction with three methods - comparison of means, correlation analysis and discriminant analysis. All three methods were used in the analysis of the same data: indicators of the potential of the human-machine interaction and G. Hofstedes six cultural dimensions. With these measurements of culture, the authors interpreted empirically the degree of its artificiality (based on the methodological assumption about the combination of natural and artificial in culture) which determines the development of artificial sociality. Based on the results of the application of three methods of statistical analysis, the authors conclude that in the contemporary world, there are both conditionally artificial cultures that are the most favourable for the development of artificial (algorithmic) sociality and conditionally natural cultures that hinder the development of artificial sociality. This type of sociality emerged under the development of writing and various methods of processing and storing information (catalogues, archives, etc.), i.e., long before the creation of machines. Artificial sociality is determined by the relative artificiality of culture, and is a prerequisite rather than a result of the human-machine interaction.

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14

Modi, Nandini, and Jaiteg Singh. "Role of Eye Tracking in Human Computer Interaction." ECS Transactions 107, no. 1 (April 24, 2022): 8211–18. http://dx.doi.org/10.1149/10701.8211ecst.

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With the invention of computers arises the need of an interface for users and interacting with a computer has become a natural practice. For all the opportunities a machine can bring, it is now a limiting factor for humans and their interaction with machines. This has given rise to a significant amount of research in the area of human computer interaction to make it more intuitive, simpler, and efficient. Human interaction with computers is no longer confined to printers and keyboards. Traditional input devices give way to natural inputs like voice, gestures, and visual computing using eye tracking. This paper provides useful insights in understanding the use of eye gaze tracking technology for human-machine interaction. A case study was conducted with 15 participants to analyze eye movements on an educational website. Visual attention was measured using eye gaze fixations data and heat maps were utilized to illustrate the results.

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15

Andrist, Sean, Dan Bohus, Bilge Mutlu, and David Schlangen. "Turn-Taking and Coordination in Human-Machine Interaction." AI Magazine 37, no. 4 (January 17, 2017): 5–6. http://dx.doi.org/10.1609/aimag.v37i4.2700.

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This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains. The contributions highlight turn-taking and coordination in human-machine interaction as an emerging and evolving research area with important implications for future applications of AI.

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16

Li, Jie. "Exploring a Human-Machine Interaction Method." International Journal of High School Research 2, no. 3 (September 1, 2020): 1–6. http://dx.doi.org/10.36838/v2i3.1.

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17

Alm, Torbjorn, Jens Alfredson, and Kjell Ohlsson. "Simulator-based human-machine interaction design." International Journal of Vehicle Systems Modelling and Testing 4, no. 1/2 (2009): 1. http://dx.doi.org/10.1504/ijvsmt.2009.029174.

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18

de Wit, Paulus A. J. M., and Roberto Moraes Cruz. "Learning from AF447: Human-machine interaction." Safety Science 112 (February 2019): 48–56. http://dx.doi.org/10.1016/j.ssci.2018.10.009.

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19

Roth, E. M., K. B. Bennett, and D. D. Woods. "Human interaction with an “intelligent” machine." International Journal of Man-Machine Studies 27, no. 5-6 (November 1987): 479–525. http://dx.doi.org/10.1016/s0020-7373(87)80012-3.

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20

Lu, Jia-ni, Hua Qian, Ai-ping Xiao, and Miao-wen Shi. "Human-machine Interaction Based on Voice." AASRI Procedia 3 (2012): 583–88. http://dx.doi.org/10.1016/j.aasri.2012.11.092.

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21

Dunne, B. J., and R. G. Jahn. "Experiments in Remote Human/Machine Interaction." EXPLORE 3, no. 3 (May 2007): 272. http://dx.doi.org/10.1016/j.explore.2007.03.025.

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22

Lindvall, Martin, Jesper Molin, and Jonas Löwgren. "From machine learning to machine teaching." Interactions 25, no. 6 (October 25, 2018): 52–57. http://dx.doi.org/10.1145/3282860.

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23

Stanley, Jeff, Ozgur Eris, and Monika Lohani. "A Conceptual Framework for Machine Self-Presentation and Trust." International Journal of Humanized Computing and Communication 2, no. 1 (March 1, 2021): 20–45. http://dx.doi.org/10.35708/hcc1869-148366.

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Increasingly, researchers are creating machines with humanlike social behaviors to elicit desired human responses such as trust and engagement, but a systematic characterization and categorization of such behaviors and their demonstrated effects is missing. This paper proposes a taxonomy of machine behavior based on what has been experimented with and documented in the literature to date. We argue that self-presentation theory, a psychosocial model of human interaction, provides a principled framework to structure existing knowledge in this domain and guide future research and development. We leverage a foundational human self-presentation taxonomy (Jones and Pittman, 1982), which associates human verbal behaviors with strategies, to guide the literature review of human-machine interaction studies we present in this paper. In our review, we identified 36 studies that have examined human-machine interactions with behaviors corresponding to strategies from the taxonomy. We analyzed frequently and infrequently used strategies to identify patterns and gaps, which led to the adaptation of Jones and Pittman’s human self-presentation taxonomy to a machine self-presentation taxonomy. The adapted taxonomy identifies strategies and behaviors machines can employ when presenting themselves to humans in order to elicit desired human responses and attitudes. Drawing from models of human trust we discuss how to apply the taxonomy to affect perceived machine trustworthiness.

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24

Chen, Shuxian, Zongqiang Ren, Xikai Yu, and Ao Huang. "A Dynamic Model of Evolutionary Knowledge and Capabilities Based on Human-Machine Interaction in Smart Manufactures." Computational Intelligence and Neuroscience 2022 (April 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/8584888.

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The increasing use of smart machines and devices is not only changing production principles but also reshaping the value of cocreation logic. The interaction between human and smart machine is the enabler of generating augmented intelligence. A system dynamics model is abstracted from smart manufacturing practices to represent the evolutionary processes of inertia, capability, and reliability induced by human-machine interaction. Human-machine interaction is conceptualized into two dimensions: technical and cognitive interaction. Simulation experiments illustrate how the improvement of human-machine interaction can leverage the dynamic capability and reduce the inertia in enterprises through multiple nonlinear feedbacks. There are two pathways to improve reliability and performance in enterprises by human-machine interaction: (1) to promote initiative innovation (change) from endogenous enabler by improving dynamic capability and (2) to promote transformation of knowledge and variation triggered by exogenous environmental changes to improve the dynamic capability for the flexibility and reliability.

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25

Ma, Xue Liang, and Li Min Yu. "Study on the Feedback Information of Man-Machine Interface." Applied Mechanics and Materials 235 (November 2012): 340–44. http://dx.doi.org/10.4028/www.scientific.net/amm.235.340.

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This paper synthesizes the human-computer interaction and feedback from two aspects of the theory of in-depth research and analysis, reveals the interactive human-machine interfaces and inner relationship: human-computer interaction is a person and" contains the computer machines" effect relationship between scene depicts; and the human-machine interface is to achieve human-computer interaction forms and methods; at the same time, the system presents a new product development new thinking - interactive guide design. The design of the man-machine interface and real significance and related method were described briefly.

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26

Zhu, Chaoyang. "Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (September 1, 2023): 01–13. http://dx.doi.org/10.17762/ijritcc.v11i7.7736.

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Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algorithm (SSA) for the automated Human-Machine Interaction. The proposed architecture is stated as a Hidden Markov model Grey Relational Salp Swarm (HMM_ GRSS). The proposed HMM_GRSS model estimates the feature vector of the variables in the virtual reality platform and compute the feature spaces. The HMM_GRSS architecture aims to estimate the feature vector of variables within the VR platform and compute the feature spaces. Hidden Markov Models are used to model the temporal behavior and dynamics of the system, allowing for predictions and understanding of the interactions. Grey Relational Analysis is employed to evaluate the relationship and relevance between variables, aiding in feature selection and optimization. The SSA helps optimize the feature spaces by simulating the collective behavior of salp swarms, improving the efficiency and effectiveness of the HMI system. The proposed HMM_GRSS architecture aims to enhance the automated HMI process in a VR platform, allowing for improved interaction and communication between humans and machines. Simulation analysis provides a significant outcome for the proposed HMM_GRSS model for the estimation Human-Machine Interaction.

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27

Sui, Zezhou, Mian Zhou, Zhikun Feng, Angelos Stefanidis, and Nan Jiang. "Language-Led Visual Grounding and Future Possibilities." Electronics 12, no. 14 (July 20, 2023): 3142. http://dx.doi.org/10.3390/electronics12143142.

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In recent years, with the rapid development of computer vision technology and the popularity of intelligent hardware, as well as the increasing demand for human–machine interaction in intelligent products, visual localization technology can help machines and humans to recognize and locate objects, thereby promoting human–machine interaction and intelligent manufacturing. At the same time, human–machine interaction is constantly evolving and improving, becoming increasingly intelligent, humanized, and efficient. In this article, a new visual localization model is proposed, and a language validation module is designed to use language information as the main information to increase the model’s interactivity. In addition, we also list the future possibilities of visual localization and provide two examples to explore the application and optimization direction of visual localization and human–machine interaction technology in practical scenarios, providing reference and guidance for relevant researchers and promoting the development and application of visual localization and human–machine interaction technology.

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28

Xu, Jiandong, Jiong Pan, Tianrui Cui, Sheng Zhang, Yi Yang, and Tian-Ling Ren. "Recent Progress of Tactile and Force Sensors for Human–Machine Interaction." Sensors 23, no. 4 (February 7, 2023): 1868. http://dx.doi.org/10.3390/s23041868.

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Human–Machine Interface (HMI) plays a key role in the interaction between people and machines, which allows people to easily and intuitively control the machine and immersively experience the virtual world of the meta-universe by virtual reality/augmented reality (VR/AR) technology. Currently, wearable skin-integrated tactile and force sensors are widely used in immersive human–machine interactions due to their ultra-thin, ultra-soft, conformal characteristics. In this paper, the recent progress of tactile and force sensors used in HMI are reviewed, including piezoresistive, capacitive, piezoelectric, triboelectric, and other sensors. Then, this paper discusses how to improve the performance of tactile and force sensors for HMI. Next, this paper summarizes the HMI for dexterous robotic manipulation and VR/AR applications. Finally, this paper summarizes and proposes the future development trend of HMI.

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29

Alonso-García, María, Ana García-Sánchez, Paula Jaén-Moreno, and Manuel Fernández-Rubio. "Performance Analysis of Urban Cleaning Devices Using Human–Machine Interaction Method." Sustainability 13, no. 11 (May 22, 2021): 5846. http://dx.doi.org/10.3390/su13115846.

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Presently, several jobs require the collaboration of humans and machines to perform different services and tasks. The ease and intuitiveness of the worker when using each machine will not only improve the worker’s experience but also improve the company’s productivity and the satisfaction that all users have. Specifically, electromechanical devices used to provide cleaning services require complex interactions. These interactions determine the usability and performance of devices. Therefore, devices must have appropriate ergonomic arrangements for human–machine interactions. Otherwise, the desired performance cannot be achieved. This study analyzes the performance of an urban cleaning device (pressure washer on a power take-off van) using human–machine interaction method. The method measures visceral and behavioral levels (set by Norman) and service times. Using these measurements, the usability of the pressure washer is determined according to different factors that facilitate the operator’s well-being in the working environment. A pressure washer from Feniks Cleaning and Safety, Limited Company, has been studied. Sixteen errors related to ergonomics, usability and safety were identified in this machine, which operates in more than 40 locations in Spain. Therefore, this study provides valuable information on the usability and performance of pressure washers, as well as possibilities for improvement.

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30

Stephens, Keri, Anastazja Harris, Amanda Hughes, Carolyn Montagnolo, Karim Nader, S. Ashley Stevens, Tara Tasuji, Yifan Xu, Hemant Purohit, and Christopher Zobel. "Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication." Human-Machine Communication 6 (July 1, 2023): 65–85. http://dx.doi.org/10.30658/hmc.6.5.

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Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perceptions of machines. Humans use scripts around training the machine—a form of behavioral anthropomorphism—to develop social relationships with them. Correcting machine-inferred data labels changes these scripts and evokes self-doubt around who is right, which substantiates the argument that this is a form of human-machine communication.

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31

XUE Zhao-hong, 薛钊鸿, 孙梓瀚 SUN Zi-han, 成泽锐 CHENG Ze-rui, 方衡 FANG Heng, 黄梓钊 HUANG Zi-zhao, 李佼洋 LI Jiao-yang, 蔡志岗 CAI Zhi-gang, and 王嘉辉 HUANG Jia-hui. "3D human-machine interaction based on human eye detection." Chinese Journal of Liquid Crystals and Displays 33, no. 11 (2018): 958–64. http://dx.doi.org/10.3788/yjyxs20183311.0958.

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32

WANG, Dangxiao, Yilei ZHENG, Teng LI, Cong PENG, Lijun WANG, and Yuru ZHANG. "Multi-modal human-machine interaction for human intelligence augmentation." SCIENTIA SINICA Informationis 48, no. 4 (April 1, 2018): 449–65. http://dx.doi.org/10.1360/n112017-00213.

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33

Strutzenberger, Diana, Ronald Hinterbichler, Florian Pauker, and Thomas Frühwirth. "Information model for human-machine (tool) interaction." Procedia CIRP 99 (2021): 98–103. http://dx.doi.org/10.1016/j.procir.2021.03.016.

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Marache-Francisco, Cathie, and Eric Brangier. "Gamification and human-machine interaction: a synthesis." Le travail humain 78, no. 2 (2015): 165. http://dx.doi.org/10.3917/th.782.0165.

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Shan, Liuting, Yaqian Liu, Xianghong Zhang, Enlong Li, Rengjian Yu, Qiming Lian, Xiang Chen, Huipeng Chen, and Tailiang Guo. "Bioinspired kinesthetic system for human-machine interaction." Nano Energy 88 (October 2021): 106283. http://dx.doi.org/10.1016/j.nanoen.2021.106283.

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36

Lu, Lijun, Chunpeng Jiang, Guosheng Hu, Jingquan Liu, and Bin Yang. "Flexible Noncontact Sensing for Human–Machine Interaction." Advanced Materials 33, no. 16 (March 8, 2021): 2100218. http://dx.doi.org/10.1002/adma.202100218.

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37

Casacuberta, Francisco, Jorge Civera, Elsa Cubel, Antonio L. Lagarda, Guy Lapalme, Elliott Macklovitch, and Enrique Vidal. "Human interaction for high-quality machine translation." Communications of the ACM 52, no. 10 (October 2009): 135–38. http://dx.doi.org/10.1145/1562764.1562798.

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38

Heard, Jamison, Julian Fortune, and Julie A. Adams. "Speech Workload Estimation for Human-Machine Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 277–81. http://dx.doi.org/10.1177/1071181319631018.

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Performing tasks quickly and accurately in dynamic and intense environments is critical, such as supervising a remotely piloted aircraft; however, these environments contain periods of low and high workload, which can decrease task performance. A system capable of intelligently adapting its interaction modality based on the human’s workload state may mitigate these undesirable workload states: underload and overload. Such a system requires mechanisms to determine accurately the human’s overall workload state and each workload component state (i.e., cognitive, physical, visual, speech, and auditory) in order to understand the current workload state’s underlying cause effectively. Existing work estimates multiple workload components, but no method estimates speech workload. This manuscript presents an algorithm for accurately estimating a human’s speech workload level using methods suitable for real-time workload assessment. The algorithm is an essential component to future adaptive human-machine interfaces.

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39

Nickerson, Raymond S. "New Methods for Modeling Human-Machine Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 4 (October 1996): 162–63. http://dx.doi.org/10.1177/154193129604000403.

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Use of the term mental model has proliferated in the discussion of human-machine interaction. Although it seems clear that humans must depend on mental models when doing problem solving in the domain of complex systems, the literature on the topic presents a confusing variety of perspectives, and there is little empirical evidence of the structure of the models people use or of how they influence human performance. The objectives of this symposium are to (a) provide a taxonomy for mental models and suggest a theory that is intended to unify what appear now to be disparate views, (b) outline an information-theoretic method for determining the structure of complex systems, and (c) describe an application of the theory and method to a process-control simulation. In the first presentation, Moray makes the case for the need for modeling methods that can deal effectively with systems of unusual complexity. In the second, Conant describes such a method. Jamieson, in the third, reports the results of an experiment in which this method was applied.

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Lee, Hyun-Soon, Miao Xu, Naveen Senniappan Karuppusamy, and Bo-Yeong Kang. "Continuous Emotion Estimation for Human Machine Interaction." Advanced Science Letters 21, no. 3 (March 1, 2015): 404–7. http://dx.doi.org/10.1166/asl.2015.5773.

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41

Moroni, Artemis, Fernando Von Zuben, and Jônatas Manzolli. "ArTbitration: Human-Machine Interaction in Artistic Domains." Leonardo 35, no. 2 (April 2002): 185–88. http://dx.doi.org/10.1162/00240940252940568.

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In this article, the authors analyze the process of humanmachine interaction in the context of artistic domains, as a framework for exploring creativity and producing results that could not be obtained without such interaction. “ArTbitration” denotes a process aimed at improving users' aesthetic judgment involving evolutionary computation and other computational intelligence methodologies. The authors interpret it as an interactive, iterative optimization process. They also suggest ArTbitration as an effective way to produce art through the efficient manipulation of information and the proper use of computational creativity to increase the complexity of the results, without neglecting the aesthetic aspects. The article emphasizes the spoken, visual and musical domains, since these are generally characterized by the lack of a systematic way to determine the quality of the result.

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42

Barker, Philip. "The Many Faces of Human-Machine Interaction." British Journal of Educational Technology 17, no. 1 (January 1986): 74–80. http://dx.doi.org/10.1111/j.1467-8535.1986.tb00497.x.

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Schlick, C. M., C. Winkelholz, F. Motz, and H. Luczak. "Self-generated complexity and human-machine interaction." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 36, no. 1 (January 2006): 220–32. http://dx.doi.org/10.1109/tsmca.2005.859096.

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Serritiello, Dora. "Human-machine interaction, methods and international standards." IEEE Instrumentation & Measurement Magazine 22, no. 1 (February 2019): 33–35. http://dx.doi.org/10.1109/mim.2019.8633349.

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Bengler, Klaus, Markus Zimmermann, Dino Bortot, Martin Kienle, and Daniel Damböck. "Interaction Principles for Cooperative Human-Machine Systems." it - Information Technology 54, no. 4 (August 2012): 157–64. http://dx.doi.org/10.1524/itit.2012.0680.

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Fang, Yinfeng, Jianhua Zhang, Naoyuki Kubota, and Houxiang Zhang. "Bio-Signal Analysis for Human Machine Interaction." International Journal of Humanoid Robotics 16, no. 04 (August 2019): 1902002. http://dx.doi.org/10.1142/s021984361902002x.

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Widrow, Bernard, Edson P. Ferreira, and Marcelo M. Lamego. "Neurointerfaces for Human-Machine Real Time Interaction." IFAC Proceedings Volumes 31, no. 4 (April 1998): 101–6. http://dx.doi.org/10.1016/s1474-6670(17)42141-0.

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Kannavara, Raghudeep, and Keith L. Shippy. "Topics in Biometric Human-Machine Interaction Security." IEEE Potentials 32, no. 6 (November 2013): 18–25. http://dx.doi.org/10.1109/mpot.2013.2248891.

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Juang, B. H. "Hands‐free human‐machine interaction with voice." Journal of the Acoustical Society of America 115, no. 5 (May 2004): 2483. http://dx.doi.org/10.1121/1.4782767.

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Takada, Yuji, Erwin R. Boer, and Tetsuo Sawaragi. "Driver assist system for human–machine interaction." Cognition, Technology & Work 19, no. 4 (October 3, 2017): 819–36. http://dx.doi.org/10.1007/s10111-017-0439-x.

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