Abstract
The paper presents the new concept on human–systems inclusion for designing, analyzing or evaluating human–machine systems and applied it to two case studies on rail flow control. Human–systems inclusion is related to the development of online learning abilities using pedagogical learning supports and aims at applying both automation-supported human and human-supported automation processes in which a human is supported by automation or automation is supported by a human, respectively. Different learning supports are proposed: accident reports, course supports, internet accesses, simulators, technical systems, etc. People are free to use or not to use them to learn alone, from the other or with the others. The first case study concerns automation-supported driving activities and focuses on the analysis of possible dissonances of use of two automated tools. The second case study consists of designing and validating a human-supported eco-driving assistance system.
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Bentivegna DC, Atkeson CG, Chenga G (2004) Learning tasks from observation and practice. Robot Auton Syst 47:163–169
Black JT (2002) Design for system success. J Manuf Syst 20(6):1–6
Boakye-Adjei K, Thamma R, Kirby ED (2015) Autonomation: the future of manufacturing. Int J Innov Sci Eng Technol 2(10):214–219
Boy GA, Narkevicius JM (2014) Unifying human centered design and systems engineering for human systems integration. In: Aiguier M, Boulanger F, Krob D, Marchal C (eds) Complex systems design and management. Springer, Cham, pp 151–162
Brunner J, Chuang E, Goldzweig C, Cain CL, Sugar C, Yano EM (2017) User-centered design to improve clinical decision support in primary care. Int J Med Informatics 104:56–64
Butler KW, Veltre DE, Brady D (2009) Implementation of active learning pedagogy comparing low-fidelity simulation versus high-fidelity simulation in pediatric nursing education. Clin Simul Nurs 5(4):129–136
Cacciabue P-C, Martinetto M (2006) A user-centred approach for designing driving support systems: the case of collision avoidance. Cogn Technol Work 8:201–214
Catmur C (2015) Understanding intentions from actions: direct perception, inference, and the roles of mirror and mentalizing systems. Conscious Cogn 36:426–433
Cattaneo LB, Chapman AR (2010) The process of empowerment—A model for use in research and practice. Am Psychol 65(7):646–659
Chella A, Dindo H, Infantino I (2006) A cognitive framework for imitation learning. Robot Auton Syst 54:403–408
Christensen T, Laegreid P (2004) Governmental autonomisation and control: the Norwegian way. Public Adm 24:129–135
Conway CM, Christiansen MH (2001) Sequential learning in non-human primates. Trends Cogn Sci 5(12):539–546
Costa NA, Holder E, MacKinnon SN (2017) Implementing human centred design in the context of a graphical user interface redesign for ship maneuvering. Int J Hum Comput Stud 100:55–65
De Martinis V, Gallo M (2013) Models and methods to optimise train speed profiles with and without energy recovery systems: a suburban test case. Procedia-Soc Behav Sci 87:222–233
Enjalbert S, Vanderhaegen F (2017) A hybrid reinforced learning system to estimate resilience indicators. Eng Appl Artif Intell 64:295–301
Faustmann G (2000) Configuration for adaptation—A human-centered approach to flexible workflow enactment. Comput Support Coop Work 9:413–434
Fénix J, Sagot J-C, Valot C, Gomes S (2008) Operator centred design: example of a new driver aid system in the field of rail transport. Cogn Technol Work 10:53–60
Goodnough K (2005) Issues in modified problem-based learning: a self-study in pre-service science-teacher education. Can J Sci Math Technol Educ 5(3):289–306
Guilbault M, Anabelle Viau-Guay A (2017) La classe inversée comme approche pédagogique en enseignement supérieur : état des connaissances scientifiques et recommandations. Revue internationale de pédagogie de l’enseignement supérieur 33(1). http://ripes.revues.org/1193
Hamani L, Wojak P, Dapsence D, La Delfa S, Vanderhaegen F (2018) Outils numériques pour la pédagogie innovante dans les transports. 21e Congrès de Maîtrise des Risques et Sûreté de Fonctionnement, λµ21, Reims, France, 16–18 octobre 2018
Hickok G (2013) Do mirror neurons subserve action understanding? Neurosci Lett 540:56–58
Hombert L, Sion S, La Delfa S, Vanderhaegen F (2018) Contrôle mutuel pour l’aide à l’éco-conduite sûre et ponctuelle en simulation ferroviaire. 21e Congrès de Maîtrise des Risques et Sûreté de Fonctionnement, λµ21, Reims, France, 16–18 octobre 2018
Inagaki T (2006) Design of human–machine interactions in light of domain-dependence of human-centered automation. Cogn Technol Work 8(3):161–167
La Delfa S, Enjalbert S, Polet P, Vanderhagen F (2016) Eco-driving command for tram-driver system. IFAC-PapersOnLine 49(19):444–449
Lago-Rodriguez A, Lopez-Alonso V, Fernández-del-Olmo M (2013) Mirror neuron system and observational learning: behavioral and neurophysiological evidence. Behav Brain Res 248:104–113
Millot P, Hoc J-M (1997) Human-machine cooperation: metaphor or possible reality? Proceedings of the 2nd European Conference on Cognitive Science, April 9–11, Manchester, UK, pp 165–174
Mondragón E, Alonso E, Kokkola N (2017) Associative learning should go deep. Trends Cogn Sci 21(11):822–825
Oztop E, Kawato M, Arbib MA (2013) Mirror neurons: functions, mechanisms and models. Neurosci Lett 540:43–55
Parmentier FBR, Muaybery MT, Huitson M, Jones DM (2008) The perceptual determinants of repetition learning in auditory space. J Memory Lang 58:978–997
Plaisance E, Belmont B, Vérillon A, Schneider C (2007) Intégration ou inclusion? Éléments pour contribuer au débat. La nouvelle revue de l’adaptation et de la scolarisation 37:159–164
Polet P, Vanderhaegen F, Zieba S (2012) Iterative learning control based tools to learn from human error. Eng Appl Artif Intell 25(7):1515–1522
Tengland P-A (2008) Empowerment: a conceptual discussion. Health Care Anal 16:77–96
Vanderhaegen F (1999) Toward a model of unreliability to study error prevention supports. Interact Comput 11:575–595
Vanderhaegen F (2012) Cooperation and learning to increase the autonomy of ADAS. Cogn Technol Work 14(1):61–69
Vanderhaegen F (2014) Dissonance engineering: a new challenge to analyse risky knowledge when using a system. Int J Comput Commun Control 9(6):750–759
Vanderhaegen F (2016a) Is there a need for human engineering in ATO? Some case studies in transport domain. In: ERA human factor seminar, Brussels, Belgium
Vanderhaegen F (2016b) A rule-based support system for dissonance discovery and control applied to car driving. Expert Syst Appl 65:361–371
Vanderhaegen F (2016c) Mirror effect based learning systems to predict human errors—application to the air traffic control. In: Proceedings of the 13th IFAC/IFIP/IFORS/IEA symposium on analysis, design, and evaluation of human–machine systems, Kyoto, Japan, pp 295–300
Vanderhaegen F (2017) Towards increased systems resilience: new challenges based on dissonance control for human reliability in Cyber-Physical and Human Systems. Annu Rev Control 44:316–322
Vanderhaegen F (2018). Pedagogical control of railway flow based on human-machine symbiosis. Poster presented at the European Rail Human and Organisational Factors Seminar, November, Valenciennes, France, p 14–15
Vanderhaegen F (2019) Pédagogie active pour l’aide à l’innovation dans les transports. In: Vanderhaegen F, Maaoui C, Sallak M, Berdjag D (Eds) In « Défis de l’automatisation des systèmes sociotechniques » . ISTE Editions Ltd, London, UK, pp 319–338
Vanderhaegen F, Carsten O (2017) Can dissonance engineering improve risk analysis of human–machine systems? Cogn Technol Work 19(1):1–12
Vanderhaegen F, Jimenez V (2018) The amazing human factors and their dissonances for autonomous cyber-physical and human systems. In: First IEEE conference on industrial cyber-physical systems, Saint-Petersbourg, Russia, 14–18 May 2018, pp 597–602
Vanderhaegen F, Richard P (2014) MissRail: a platform dedicated to training and research in railway systems. Proceedings of the International Conference HCII, 22–27 June 2014, Creta Maris, Heraklion, Crete, Greece, pp 544–549
Vanderhaegen F, Zieba S (2014) Reinforced learning systems based on merged and cumulative knowledge to predict human actions. Inf Sci 276(20):146–159
Vanderhaegen F, Chalmé S, Anceaux F, Millot P (2006) Principles of cooperation and competition—Application to car driver behavior analysis. Cogn Technol Work 8:183–192
Vanderhaegen F, Polet P, Zieba S (2009) A reinforced iterative formalism to learn from human errors and uncertainty. Eng Appl Artif Intell 22(4–5):654–659
Vanderhaegen F, Zieba S, Enjalbert S, Polet P (2011) A benefit/cost/deficit (BCD) model for learning from human errors. Reliab Eng Syst Saf 96(7):757–766
Vanderhaegen F, Wolff M, Ibarboure S, Mollard R (2019). Heart-computer synchronization interface to control human-machine symbiosis: a new human availability support for cooperative systems. Proceedings of the 14th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human–Machine Systems, Tallinn, Estonia, September pp 16–19
Vislie L (2003) From integration to inclusion: focusing global trends and changes in the western European societies. Eur J Spec Needs Educ 18(1):17–35
Walraven E, Spaan MTJ, Bakker B (2016) Traffic flow optimization: a reinforcement learning approach. Eng Appl Artif Intell 52:203–212
Zhang C, Tang P, Cooke N, Buchanan V, Yilmaz A, St. Germain SW, Boring RL, Akca-Hobbins S, Gupta A (2017) Human-centered automation for resilient nuclear power plant outage control. Autom Constr 82:179–192
Zhang M-Y, Tian G-H, Li C-C, Gong J (2018) Learning to transform service instructions into actions with reinforcement learning and knowledge base. Int J Autom Comput 15(5):582–592
Zhou L, Li Y, Bai S (2017) A human-centered design optimization approach for robotic exoskeletons through biomechanical simulation. Robot Auton Syst 91:337–347
Acknowledgements
The present research work has been supported by the Scientific Research Network on Integrated Automation and Human–Machine Systems (GRAISyHM), and by the Regional Council of “Hauts-de-France” (Regional Council of Nord, Pas de Calais, Picardie from France), project CONPETISES (Pedagogical control of human driving tasks by automated systems). The author gratefully acknowledges the support of these institutions.
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Vanderhaegen, F. Pedagogical learning supports based on human–systems inclusion applied to rail flow control. Cogn Tech Work 23, 193–202 (2021). https://doi.org/10.1007/s10111-019-00602-2
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DOI: https://doi.org/10.1007/s10111-019-00602-2