Abstract
Human-Machine Cooperation involves multiple fields and disciplines such as automated vehicles, robots, machine manufacturing, psychological cognition, and artificial intelligence. As research in these areas progresses rapidly, it is important to keep up with new trends and key turning points in the development of collective knowledge. Based on the visualization method, we can quickly sort out the ins and outs of this field in the vast literature and search for valuable and potential literature. First, we introduced the principles of scientific visualization of knowledge graphs. Then we extracted 3257 articles from Web of Science Core Collection and established a research field of bibliographic records of representative data sets. Next, we carried out a visual analysis of the discipline Dual-Map overlay, Co-words, Co-citation, and reviewed some key Highly-Cited documents. Finally, we summarize and analyze the literature with high betweenness centrality, citation bursts, and Sigma value. This review will help professionals to have a more systematic understanding of the entire field and find opportunities for future Human-Machine Cooperation development.
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References
Hoc, J.-M.: From human–machine interaction to human–machine cooperation. Ergonomics 43(7), 833–843 (2000)
Naujoks, F., Forster, Y., Wiedemann, K., Neukum, A.: A human-machine interface for cooperative highly automated driving. In: Stanton, N., Landry, S., Di Bucchianico, G., Vallicelli, A. (eds.) Advances in Human Aspects of Transportation. AISC, vol. 484, pp. 585–595. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-41682-3_49
Biondi, F., Alvarez, I., Jeong, K.-A.: Human-vehicle cooperation in automated driving: a multidisciplinary review and appraisal. Int. J. Hum.-Comput. Interact. 35(11), 932–946 (2019)
Kuhn, T.S.: The Structure of Scientific Revolutions. University of Chicago Press (1962)
Burt, R.S.: Structural holes and good ideas. Am. J. Sociol. 110(2), 349–399 (2004)
Chen, C.: The centrality of pivotal points in the evolution of scientific networks. In: Amant, R.S., Riedl, J., Jameson, A. (eds.) IUI 2005, pp. 98–105. ACM (2005)
Kleinberg, J.: Bursty and hierarchical structure in streams. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press (2002)
Chaomei, C.: Science mapping: a systematic review of the literature. J. Data Inf. Sci. 2, 1–40 (2017)
Chen, C.: Predictive effects of structural variation on citation counts. J. Am. Soc. Inf. Sci. Technol. 63(3), 431–449 (2012)
Navarro, J., Mars, F., Young, M.S.: Lateral control assistance in car driving: classification, review and future prospects. IET Intell. Transp. Syst. 5(3), 207–220 (2011)
Flemisch, F.O., et al.: Towards cooperative guidance and control of highly automated vehicles: H-Mode and Conduct-by-Wire. Ergonomics 57(3), 343–360 (2014)
Flemisch, F., et al.: Shared control is the sharp end of cooperation: Towards a common framework of joint action, shared control and human machine cooperation. IFAC-PapersOnLine 49(19), 72–77 (2016)
Abbink, D.A., Mulder, M., Boer, E.R.: Haptic shared control: smoothly shifting control authority? Cogn. Technol. Work 14(1), 19–28 (2012)
Mulder, M., Abbink, D.A., Boer, E.R.: Sharing control with haptics: seamless driver support from manual to automatic control. Hum. Factors 54(5), 786–798 (2012)
Flemisch, F., et al.: Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations. Cogn. Technol. Work 14(1), 3–18 (2012)
Merat, N., et al.: Transition to manual: driver behaviour when resuming control from a highly automated vehicle. Transp. Res. Part F: Traffic Psychol. Behav. 27, 274–282 (2014)
Itoh, M., Inagaki, T.: Design and evaluation of steering protection for avoiding collisions during a lane change. Ergonomics 57(3), 361–373 (2014)
Mars, F., Deroo, M., Hoc, J.: Analysis of human-machine cooperation when driving with different degrees of haptic shared control. IEEE Trans. Haptics 7(3), 324–333 (2014)
Blanco, M., et al.: Automated vehicles: take-over request and system prompt evaluation. In: Meyer, G., Beiker, S. (eds.) Road Vehicle Automation 3. LNM, pp. 111–119. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40503-2_9
Navarro, J.: Human–machine interaction theories and lane departure warnings. Theor. Issues Ergon. Sci. 18(6), 519–547 (2017)
Michalos, G., et al.: Design considerations for safe human-robot collaborative workplaces. Procedia CIRP 37, 248–253 (2015)
Zanchettin, A.M., et al.: Safety in human-robot collaborative manufacturing environments: metrics and control. IEEE Trans. Autom. Sci. Eng. 13(2), 882–893 (2016)
Woolley, A.W., et al.: Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004), 686–688 (2010)
Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. J. Commun. ACM 60(6), 84–90 (2017)
Quinn, A.J., Bederson, B.B.: Human computation: a survey and taxonomy of a growing field. In: 29th Annual Chi Conference on Human Factors in Computing Systems, pp. 1403–1412 (2011)
Zou, H., et al.: Robust WiFi-enabled device-free gesture recognition via unsupervised adversarial domain adaptation, pp. 1–8 (2018)
Albini, A., Denei, S., Cannata, G.: Human hand recognition from robotic skin measurements in human-robot physical interactions, pp. 4348–4353 (2017)
Wang, P., et al.: Deep learning-based human motion recognition for predictive context-aware human-robot collaboration. CIRP Ann. 67(1), 17–20 (2018)
Zhong, Y., Deng, W.: Deep difference analysis in similar-looking face recognition, pp. 3353–3358 (2018)
Wang, F.: The emergence of intelligent enterprises: from CPS to CPSS. IEEE Intell. Syst. 25(4), 85–88 (2010)
Hoc, J.-M.: Towards a cognitive approach to human–machine cooperation in dynamic situations. Int. J. Hum.-Comput. Stud. 54(4), 509–540 (2001)
Pacaux, M.P., et al.: Levels of automation and human-machine cooperation: application to human-robot interaction. IFAC Proc. Volumes 44(1), 6484–6492 (2011)
Krüger, J., Lien, T.K., Verl, A.: Cooperation of human and machines in assembly lines. CIRP Ann. 58(2), 628–646 (2009)
Panchanathan, K., Boyd, R.: Indirect reciprocity can stabilize cooperation without the second-order free rider problem. Nature 432(7016), 499–502 (2004)
Fehr, E., Gächter, S.: Altruistic punishment in humans. Nature 415(6868), 137–140 (2002)
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Liu, Y. (2020). Reviewing and Predicting Human-Machine Cooperation Based on Knowledge Graph Analysis. In: Stephanidis, C., Marcus, A., Rosenzweig, E., Rau, PL.P., Moallem, A., Rauterberg, M. (eds) HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies. HCII 2020. Lecture Notes in Computer Science(), vol 12423. Springer, Cham. https://doi.org/10.1007/978-3-030-60114-0_12
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