Skip to main content

Advertisement

Log in

Human-machine Collaborative Decision-making: An Evolutionary Roadmap Based on Cognitive Intelligence

  • Review
  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

With the development of artificial intelligence technology, intelligent machines are increasingly equipped with human-like abilities such as autonomous decision-making, reasoning, active interaction, and situation awareness. Intelligent machines can act as peers to humans and collaborate with humans to complete decision tasks. The ability to collaborate with humans has become an indicator of the intelligence level of a machine, and determines the scope and depth of its applications. Human-machine collaborative decision-making has attracted attentions from multi-disciplines in recent years, and the diverse origins of its developments make the mechanism of collaborative decision-making ambiguous. A thorough combing of the evolution of human-machine collaboration based on cognitive intelligence is of great importance for understanding the nature of human-machine collaboration at the decision layer and guiding future studies. This article makes a retrospect on the evolution of human-machine collaborative decision-making based on cognition intelligence. It summarizes current research in three categories: the human-machine collaborative system implementation, the human-machine intelligence integrative mechanism and the human-machine interaction in collaborative decision-making process. It reveals the roadmap of the evolution of intelligent machines toward human-machine integration intelligence. Based on the roadmap, prospects for future research of human-machine collaborative decision-making are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Availability of Data and Materials

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Haenlein M, Kaplan A (2019) A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif Manag Rev 61(4):5–14

    Article  Google Scholar 

  2. Jarrahi MH (2018) Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Bus Horiz 61(4):577–586

    Article  Google Scholar 

  3. Kumar S, Savur C, Sahin F (2020) Survey of human–robot collaboration in industrial settings: awareness, intelligence, and compliance. IEEE Trans Syst Man Cybernetics: Syst 51(1):280–297

    Article  Google Scholar 

  4. Xu W (2019) Toward human-centered AI: a perspective from human-computer interaction. Interactions 26(4):42–46

    Article  Google Scholar 

  5. Chi OH, Jia S, Li Y, Gursoy D (2021) Developing a formative scale to measure consumers’ trust toward interaction with artificially intelligent (AI) social robots in service delivery. Comput Hum Behav 118:106700

    Article  Google Scholar 

  6. Malik AA, Brem A (2021) Digital twins for collaborative robots: a case study in human-robot interaction. Robot Comput Integr Manuf 68:102092

    Article  Google Scholar 

  7. Khadpe P, Krishna R, Fei-Fei L, Hancock JT, Bernstein MS (2020) Conceptual metaphors impact perceptions of human-AI collaboration. In the Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2):1–26

  8. De Giorgio A, Romero M, Onori M, Wang L (2017) Human-machine collaboration in virtual reality for adaptive production engineering. Procedia Manuf 11:1279–1287

    Article  Google Scholar 

  9. Cai H, Lin Y (2011) Coordinating cognitive assistance with cognitive engagement control approaches in human–machine collaboration. IEEE Trans Syst Man Cybernetics-part A: Syst Hum 42(2):286–294

    Article  Google Scholar 

  10. Avila-Munoz R, Clemente-Mediavilla J, Perez-Luque-Maricalva MJ (2021) Communicative functions in human-computer Interface Design: a taxonomy of functional animation. Rev Communication Res 9:119–146

    Article  Google Scholar 

  11. Liu Y, Sivaparthipan CB, Shankar A (2022) Human–computer interaction based visual feedback system for augmentative and alternative communication. Int J Speech Technol 25(2):305–314

    Article  Google Scholar 

  12. Wenskovitch J, North C (2020) Interactive Artificial Intelligence: Designing for the. Two Black Boxes” Problem Computer 53(8):29–39

    Google Scholar 

  13. Ajoudani A, Zanchettin AM, Ivaldi S, Albu-Schäffer A, Kosuge K, Khatib O (2018) Progress and prospects of the human–robot collaboration. Auton Robots 42(5):957–975

    Article  Google Scholar 

  14. De Visser EJ, Pak R, Shaw TH (2018) From ‘automation’ to ‘autonomy’: the importance of trust repair in human–machine interaction. Ergonomics 61(10):1409–1427

    Article  Google Scholar 

  15. Formosa P (2021) Robot Autonomy vs. Human Autonomy: Social Robots, Artificial Intelligence (AI), and the nature of autonomy. Mind Mach 31(4):595–616

    Article  Google Scholar 

  16. Kridalukmana R, Lu HY, Naderpour M (2020) A supportive situation awareness model for human-autonomy teaming in collaborative driving. Theoretical Issues in Ergonomics Science 21(6):658–683

    Article  Google Scholar 

  17. Puranam P (2021) Human–AI collaborative decision-making as an organization design problem. J Organ Des 10(2):75–80

    Google Scholar 

  18. Birhane A (2021) The impossibility of automating ambiguity. Artif Life 27(1):44–61

    Article  Google Scholar 

  19. Lv C, Li Y, Xing Y, Huang C, Cao D, Zhao Y, Liu Y (2021) Human–machine collaboration for automated driving using an Intelligent two-phase haptic interface. Adv Intell Syst 3(4):2000229

    Article  Google Scholar 

  20. Liu H, Liu T, Zhang Z, Sangaiah AK, Yang B, Li Y (2022) Arhpe: asymmetric relation-aware representation learning for head pose estimation in industrial human–computer interaction. IEEE Trans Industr Inf 18(10):7107–7117

    Article  Google Scholar 

  21. Perula-Martinez R, Castro-Gonzalez A, Malfaz M, Alonso-Martin F, Salichs MA (2019) Bioinspired decision-making for a socially interactive robot. Cogn Syst Res 54:287–301

    Article  Google Scholar 

  22. Salomon G, Perkins DN, Globerson T (1991) Partners in cognition: extending human intelligence with intelligent technologies. Educational Researcher 20(3):2–9

    Article  Google Scholar 

  23. Jiao J, Zhou F, Gebraeel NZ, Duffy V (2020) Towards augmenting cyber-physical-human collaborative cognition for human-automation interaction in complex manufacturing and operational environments. Int J Prod Res 58(16):5089–5111

    Article  Google Scholar 

  24. Lemaignan S, Warnier M, Sisbot EA, Clodic A, Alami R (2017) Artificial cognition for social human–robot interaction: an implementation. Artif Intell 247:45–69

    Article  MathSciNet  Google Scholar 

  25. Uslu S, Kaur D, Rivera SJ, Durresi A, Babbar-Sebens M, Tilt JH (2021) A trustworthy human–machine framework for collective decision making in food–energy–water management: the role of trust sensitivity. Knowl Based Syst 213:106683

    Article  Google Scholar 

  26. Yun Y, Ma D, Yang M (2021) Human–computer interaction-based decision support system with applications in data mining. Future Generation Computer Systems 114:285–289

    Article  Google Scholar 

  27. Walsh C (2018) Human-in-the-loop development of soft wearable robots. Nat Reviews Mater 3(6):78–80

    Article  Google Scholar 

  28. Lv Z, Qiao L, Singh AK (2020) Advanced machine learning on cognitive computing for human behavior analysis. IEEE Trans Comput Social Syst 8(5):1194–1202

    Article  Google Scholar 

  29. Liu Z, Wang J (2020) Human-cyber-physical systems: concepts, challenges, and research opportunities. Front Inform Technol Electron Eng 21(11):1535–1553

    Article  Google Scholar 

  30. Lin K, Li Y, Sun J, Zhou D, Zhang Q (2020) Multi-sensor fusion for body sensor network in medical human–robot interaction scenario. Inform Fusion 57:15–26

    Article  Google Scholar 

  31. Shen Z, Elibol A, Chong NY (2021) Multi-modal feature fusion for better understanding of human personality traits in social human–robot interaction. Robot Auton Syst 146:103874

    Article  Google Scholar 

  32. Gebru B, Zeleke L, Blankson D, Nabil M, Nateghi S, Homaifar A, Tunstel E (2022) A review on human–machine trust evaluation: human-centric and machine-centric perspectives. IEEE Trans Human-Machine Syst 52(5):952–962

    Article  Google Scholar 

  33. Chen M, Nikolaidis S, Soh H, Hsu D, Srinivasa S (2020) Trust-aware decision making for human-robot collaboration: model learning and planning. ACM Trans Human-Robot Interact 9(2):1–23

    Article  Google Scholar 

  34. Rudin C, Chen C, Chen Z, Huang H, Semenova L, Zhong C (2022) Interpretable machine learning: fundamental principles and 10 grand challenges. Stat Surv 16:1–85

    Article  MathSciNet  MATH  Google Scholar 

  35. Vinuesa R, Sirmacek B (2021) Interpretable deep-learning models to help achieve the Sustainable Development Goals. Nat Mach Intell 3(11):926–926

    Article  Google Scholar 

  36. Van Ments L, Treur J (2021) Reflections on dynamics, adaptation and control: a cognitive architecture for mental models. Cogn Syst Res 70:1–9

    Article  MATH  Google Scholar 

  37. Dushkin RV, Stepankov VY (2021) Hybrid bionic cognitive architecture for artificial general intelligence agents. Procedia Comput Sci 190:226–230

    Article  Google Scholar 

  38. Samsonovich AV (2020) Socially emotional brain-inspired cognitive architecture framework for artificial intelligence. Cogn Syst Res 60:57–76

    Article  Google Scholar 

  39. Hoc JM (2000) From human–machine interaction to human–machine cooperation. Ergonomics 43(7):833–843

    Article  Google Scholar 

  40. Seeber I, Bittner E, Briggs RO et al (2020) Machines as teammates: a research agenda on AI in team collaboration. Inf Manag 57(2):103174

    Article  Google Scholar 

  41. Heer J (2019) Agency plus automation: Designing artificial intelligence into interactive systems. Proc Natl Acad Sci 116(6):1844–1850

    Article  Google Scholar 

  42. Xing Y, Lv C, Cao D, Hang P (2021) Toward human-vehicle collaboration: review and perspectives on human-centered collaborative automated driving. Transp Res Part C: Emerg Technol 128:103199

    Article  Google Scholar 

  43. Youyou W, Kosinski M, Stillwell D (2015) Computer-based personality judgments are more accurate than those made by humans. Proc Natl Acad Sci 112(4):1036–1040

    Article  Google Scholar 

  44. Cai H (2021) Reaching consensus with human beings through blockchain as an ethical rule of strong artificial intelligence. AI and Ethics 1(1):55–59

    Article  Google Scholar 

  45. McNeese NJ, Demir M, Cooke NJ, She M (2021) Team Situation Awareness and Conflict: a study of human–machine teaming. J Cogn Eng Decis Mak 15(2–3):83–96

    Article  Google Scholar 

  46. Nicora ML, Ambrosetti R, Wiens GJ, Fassi I (2021) Human–Robot collaboration in Smart Manufacturing: Robot reactive Behavior Intelligence. J Manuf Sci Eng 143(3):031009

    Article  Google Scholar 

  47. Hanly EJ, Miller BE, Kumar R et al (2006) Mentoring console improves collaboration and teaching in surgical robotics. J Laparoendosc Adv Surg Tech 16(5):445–451

    Article  Google Scholar 

  48. Licklider JC (1960) Man-computer symbiosis. IRE transactions on human factors in Electronics, HFE-. 1(1):4–11

  49. Zhai Z, Martínez JF, Beltran V, Martínez NL (2020) Decision support systems for agriculture 4.0: Survey and challenges. Comput Electron Agric 170:105256

    Article  Google Scholar 

  50. Silva de Oliveira C, Sanin C, Szczerbicki E (2022) Smart Knowledge Engineering for Cognitive Systems: a brief overview. Cybernetics and Systems 53(5):384–402

    Article  Google Scholar 

  51. Zhou J, Zhou Y, Wang B, Zang J (2019) Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering 5(4):624–636

    Article  Google Scholar 

  52. Shaw MJ (1993) Machine learning methods for intelligent decision support: an introduction. Decis Support Syst 10(2):79–83

    Article  MathSciNet  Google Scholar 

  53. Farshid M, Paschen J, Eriksson T, Kietzmann J (2018) Go boldly!: explore augmented reality (AR), virtual reality (VR), and mixed reality (MR) for business. Bus Horiz 61(5):657–663

    Article  Google Scholar 

  54. Qian Xue-sen Yu Jing-yuan, Dai Ru-wei. (1990). A new field of science-open complex giant system and its methodology. Chin J Nat, 13(1):3–10

  55. Duan Y, Edwards JS, Dwivedi YK (2019) Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. Int J Inf Manag 48:63–71

    Article  Google Scholar 

  56. Shneiderman B (2020) Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Trans Interact Intell Syst (TiiS) 10(4):1–31

    Article  Google Scholar 

  57. Zheng NN, Liu ZY, Ren PJ, Ma YQ, Chen ST, Yu SY, Xue JR, Chen BD, Wang FY (2017) Hybrid-augmented intelligence: collaboration and cognition. Front Inform Technol Electron Eng 18(2):153–179

    Article  Google Scholar 

  58. Zanzotto FM (2019) Human-in-the-loop Artificial Intelligence. J Artif Intell Res 64:243–252

    Article  MathSciNet  Google Scholar 

  59. Wu X, Xiao L, Sun Y, Zhang J, Ma T, He L (2022) A survey of human-in-the-loop for machine learning. Future Generation Computer Systems 135:364–381

    Article  Google Scholar 

  60. Li HY, Paranawithana I, Yang L, Lim TSK, Foong S, Ng FC, Tan UX (2018) Stable and compliant motion of physical human–robot interaction coupled with a moving environment using variable admittance and adaptive control. IEEE Rob Autom Lett 3(3):2493–2500

    Article  Google Scholar 

  61. Li Y, Cui R, Yan W, Xu D (2019) Long-term adaptive informative path planning for scalar field monitoring using cross-entropy optimization. Sci China Inform Sci 62(5):1–3

    Article  Google Scholar 

  62. Sloman S (2005) Causal models: how people think about the world and its alternatives. Oxford University Press

  63. Salvi C, Bricolo E, Kounios J, Bowden E, Beeman M (2016) Insight solutions are correct more often than analytic solutions. Think Reason 22(4):443–460

    Article  Google Scholar 

  64. Nissen MJ, Bullemer P (1987) Attentional requirements of learning: evidence from performance measures. Cogn Psychol 19(1):1–32

    Article  Google Scholar 

  65. Norman KA, O’Reilly RC (2003) Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychol Rev 110(4):611

    Article  Google Scholar 

  66. Haesevoets T, De Cremer D, Dierckx K, Van Hiel A (2021) Human-machine collaboration in managerial decision making. Comput Hum Behav 119:106730

    Article  Google Scholar 

  67. Tran D, Du J, Sheng W, Osipychev D, Sun Y, Bai H (2018) A human-vehicle collaborative driving framework for driver assistance. IEEE Trans Intell Transp Syst 20(9):3470–3485

    Article  Google Scholar 

  68. Chryssolouris G, Mavrikios D, Papakostas N, Mourtzis D, Michalos G, Georgoulias K (2009) Digital manufacturing: history, perspectives, and outlook. Proc Institution Mech Eng Part B: J Eng Manuf 223(5):451–462

    Article  Google Scholar 

  69. Krugh M, Mears L (2018) A complementary cyber-human systems framework for industry 4.0 cyber-physical systems. Manuf Lett 15:89–92

    Article  Google Scholar 

  70. Fonseca F, Marcinkowski M, Davis C (2019) Cyber-human systems of thought and understanding. J Association Inform Sci Technol 70(4):402–411

    Google Scholar 

  71. Kusiak A (2017) Smart manufacturing must embrace big data. Nat News 544(7648):23

    Article  Google Scholar 

  72. Liu S, Duffy AH, Whitfield RI, Boyle IM (2010) Integration of decision support systems to improve decision support performance. Knowl Inf Syst 22(3):261–286

    Article  Google Scholar 

  73. Shin D (2021) The effects of explainability and causability on perception, trust, and acceptance: implications for explainable AI. Int J Hum Comput Stud 146:102551

    Article  Google Scholar 

  74. Arrieta AB, Díaz-Rodríguez N, Del Ser J et al (2020) Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inform Fusion 58:82–115

    Article  Google Scholar 

  75. Fügener A, Grahl J, Gupta A, Ketter W (2022) Cognitive challenges in human–artificial intelligence collaboration: investigating the path toward productive delegation. Inform Syst Res 33(2):678–696

    Article  Google Scholar 

  76. Bryant DJ (2006) Rethinking OODA: toward a modern cognitive framework of command decision making. Military Psychol 18(3):183–206

    Article  Google Scholar 

  77. Groen FC, Pavlin G, Winterboer A, Evers V (2017) A hybrid approach to decision making and information fusion: combining humans and artificial agents. Robot Auton Syst 90:71–85

    Article  Google Scholar 

  78. Liu Z, Cai Y, Wang H, Chen L, Gao H, Jia Y, Li Y (2021) Robust target recognition and tracking of self-driving cars with radar and camera information fusion under severe weather conditions. IEEE Trans Intell Transp Syst 23(7):6640–6653

    Article  Google Scholar 

  79. Burks L, Ahmed N, Loefgren I, Barbier L, Muesing J, McGinley J, Vunnam S (2021) Collaborative human-autonomy semantic sensing through structured POMDP planning. Robot Auton Syst 140:103753

    Article  Google Scholar 

  80. Zhang FS, Ge DY, Song J, Xiang WJ (2022) Outdoor scene understanding of mobile robot via multi-sensor information fusion. J Industrial Inform Integr 30:100392

    Article  Google Scholar 

  81. Liu S, Wang L, Wang XV, Cooper C, Gao RX (2021) Leveraging multimodal data for intuitive robot control towards human-robot collaborative assembly. Procedia CIRP 104:206–211

    Article  Google Scholar 

  82. Naderpour M, Lu J, Zhang G (2014) An intelligent situation awareness support system for safety-critical environments. Decis Support Syst 59:325–340

    Article  Google Scholar 

  83. Kokar MM, Matheus CJ, Baclawski K (2009) Ontology-based situation awareness. Inform Fusion 10(1):83–98

    Article  Google Scholar 

  84. Chen S, Jian Z, Huang Y, Chen Y, Zhou Z, Zheng N (2019) Autonomous driving: cognitive construction and situation understanding. Sci China Inform Sci 62(8):1–27

    Article  Google Scholar 

  85. Stubbs K, Hinds PJ, Wettergreen D (2007) Autonomy and common ground in human-robot interaction: a field study. IEEE Intell Syst 22(2):42–50

    Article  Google Scholar 

  86. Endsley MR (2022) Supporting Human-AI teams: transparency, explainability, and situation awareness. Comput Hum Behav 140:107574

    Article  Google Scholar 

  87. Rosenfeld A, Richardson A (2019) Explainability in human–agent systems. Auton Agent Multi-Agent Syst 33(6):673–705

    Article  Google Scholar 

  88. Vellido A (2020) The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Comput Appl 32(24):18069–18083

    Article  Google Scholar 

  89. Minh D, Wang HX, Li YF, Nguyen TN (2022) Explainable artificial intelligence: a comprehensive review. Artif Intell Rev 55(5):3503–3568

    Article  Google Scholar 

  90. Weitz K, Schiller D, Schlagowski R, Huber T, André E (2021) Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design. J Multimodal User Interfaces 15(2):87–98

    Article  Google Scholar 

  91. Sisbot EA, Marin-Urias LF, Alami R, Simeon T (2007) A human aware mobile robot motion planner. IEEE Trans Robot 23(5):874–883

    Article  Google Scholar 

  92. Wang D, Yang Q, Abdul A, Lim BY (2019), May Designing theory-driven user-centric explainable AI. In Proceedings of the 2019 CHI conference on human factors in computing systems (CHI ‘19), Association for Computing Machinery, New York, USA, 601:1–15

  93. Mao H, Liu W, Hao J et al (2020), April Neighborhood cognition consistent multi-agent reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, 34(05):7219–7226

  94. Garnelo M, Shanahan M (2019) Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Curr Opin Behav Sci 29:17–23

    Article  Google Scholar 

  95. Silver D, Huang A, Maddison CJ et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484–489

    Article  Google Scholar 

  96. Floridi L, Chiriatti M (2020) GPT-3: its nature, scope, limits, and consequences. Mind Mach 30(4):681–694

    Article  Google Scholar 

  97. Jumper J, Evans R, Pritzel A, et.al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589

    Article  Google Scholar 

  98. Heaven D (2019) Why deep-learning AIs are so easy to fool. Nature 574(7777):163–166

    Article  Google Scholar 

  99. Gunning D, Aha D (2019) DARPA’s explainable artificial intelligence (XAI) program. AI Magazine 40(2):44–58

    Article  Google Scholar 

  100. Miller T (2019) Explanation in artificial intelligence: insights from the social sciences. Artif Intell 267:1–38

    Article  MathSciNet  MATH  Google Scholar 

  101. Cross ES, Ramsey R (2021) Mind meets machine: towards a cognitive science of human–machine interactions. Trends Cogn Sci 25(3):200–212

    Article  Google Scholar 

  102. Lieto A, Bhatt M, Oltramari A, Vernon D (2018) The role of cognitive architectures in general artificial intelligence. Cogn Syst Res 48:1–3

    Article  Google Scholar 

  103. Cheng G, Ramirez-Amaro K, Beetz M, Kuniyoshi Y (2019) Purposive learning: Robot reasoning about the meanings of human activities. Sci Rob 4(26):eaav1530

    Article  Google Scholar 

  104. Costa E, Guidotti A (1991) Diazepam binding inhibitor (DBI): a peptide with multiple biological actions. Life Sci 49(5):325–344

    Article  Google Scholar 

  105. Anderson JR, Matessa M, Lebiere C (1997) ACT-R: a theory of higher level cognition and its relation to visual attention. Human–Computer Interact 12(4):439–462

    Article  Google Scholar 

  106. Laird JE, Newell A, Rosenbloom PS (1987) Soar: an architecture for general intelligence. Artif Intell 33(1):1–64

    Article  Google Scholar 

  107. Laird JE, Lebiere C, Rosenbloom PS (2017) A standard model of the mind: toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. Ai Magazine 38(4):13–26

    Article  Google Scholar 

  108. Luo Y, Xiang Y, Cao K, Li K (2016) A dynamic automated lane change maneuver based on vehicle-to-vehicle communication. Transp Res Part C: Emerg Technol 62:87–102

    Article  Google Scholar 

  109. Paulius D, Sun Y (2019) A survey of knowledge representation in service robotics. Robot Auton Syst 118:13–30

    Article  Google Scholar 

  110. Nardo M, Forino D, Murino T (2020) The evolution of man–machine interaction: the role of human in industry 4.0 paradigm. Prod Manuf Res 8(1):20–34

    Google Scholar 

  111. Erol BA, Majumdar A, Benavidez P, Rad P, Choo KKR, Jamshidi M (2019) Toward artificial emotional intelligence for cooperative social human–machine interaction. IEEE Trans Comput Social Syst 7(1):234–246

    Article  Google Scholar 

  112. Kolling A, Walker P, Chakraborty N, Sycara K, Lewis M (2015) Human interaction with robot swarms: a survey. IEEE Trans Human-Machine Syst 46(1):9–26

    Article  Google Scholar 

  113. Norman DA (1994) How might people interact with agents. Commun ACM 37(7):68–71

    Article  Google Scholar 

  114. Höök K (2000) Steps to take before intelligent user interfaces become real. Interact Comput 12(4):409–426

    Article  Google Scholar 

  115. Marchiori D, Warglien M (2008) Predicting human interactive learning by regret-driven neural networks. Science 319(5866):1111–1113

    Article  Google Scholar 

  116. López-González M (2019) Today is to see and know: An argument and proposal for integrating human cognitive intelligence into autonomous vehicle perception. Electronic Imaging, 2019(15):54 – 1

  117. Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction. Science 350(6266):1332–1338

    Article  MathSciNet  MATH  Google Scholar 

  118. Li D, Liu M, Zhao F, Liu Y (2019) Challenges and countermeasures of interaction in autonomous vehicles. Sci China Inform Sci 62(5):050201

    Article  Google Scholar 

  119. Koppula HS, Saxena A (2015) Anticipating human activities using object affordances for reactive robotic response. IEEE Trans Pattern Anal Mach Intell 38(1):14–29

    Article  Google Scholar 

  120. Tahboub KA (2006) Intelligent human-machine interaction based on dynamic bayesian networks probabilistic intention recognition. J Intell Rob Syst 45(1):31–52

    Article  Google Scholar 

  121. Følstad A, Engen V, Haugstveit IM, Pickering JB (2018) Automation in human-machine networks: how increasing machine agency affects human agency. In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds) Man-Machine Interactions 5. ICMMI 2017. Advances in Intelligent Systems and Computing, 659:72–81

  122. Tabrez A, Luebbers MB, Hayes B (2020) A survey of mental modeling techniques in human–robot teaming. Curr Rob Rep 1(4):259–267

    Article  Google Scholar 

  123. Bansal G, Nushi B, Kamar E, Lasecki WS, Weld DS, Horvitz E (2019), October Beyond accuracy: The role of mental models in human-AI team performance. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7(1):2–11

  124. Lauer T, Welsch R, Abbas SR, Henke M (2019), July Behavioral Analysis of Human-Machine Interaction in the Context of Demand Planning Decisions. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, 965:130–141

  125. Coca-Vila I (2018) Self-driving cars in dilemmatic situations: an approach based on the theory of justification in criminal law. Crim Law Philos 12(1):59–82

    Article  Google Scholar 

  126. Iphofen R, Kritikos M (2021) Regulating artificial intelligence and robotics: ethics by design in a digital society. Contemp Social Sci 16(2):170–184

    Article  Google Scholar 

  127. Wirtz BW, Weyerer JC, Geyer C (2019) Artificial intelligence and the public sector—applications and challenges. Int J Public Adm 42(7):596–615

    Article  Google Scholar 

  128. Muir BM (1994) Trust in automation: part I. theoretical issues in the study of trust and human intervention in automated systems. Ergonomics 37(11):1905–1922

    Article  Google Scholar 

  129. Lee JD, See KA (2004) Trust in automation: Designing for appropriate reliance. Hum Factors 46(1):50–80

    Article  Google Scholar 

  130. Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors 37(1):32–64

    Article  Google Scholar 

  131. Azevedo CR, Raizer K, Souza R (2017), March A vision for human-machine mutual understanding, trust establishment, and collaboration. In 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), Savannah, GA, USA, 1–3

  132. Martinez CM, Heucke M, Wang FY, Gao B, Cao D (2017) Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey. IEEE Trans Intell Transp Syst 19(3):666–676

    Article  Google Scholar 

  133. Arbabzadeh N, Jafari M (2017) A data-driven approach for driving safety risk prediction using driver behavior and roadway information data. IEEE Trans Intell Transp Syst 19(2):446–460

    Article  Google Scholar 

  134. Morton J, Wheeler TA, Kochenderfer MJ (2016) Analysis of recurrent neural networks for probabilistic modeling of driver behavior. IEEE Trans Intell Transp Syst 18(5):1289–1298

    Article  Google Scholar 

  135. Merat N, Jamson AH, Lai FC, Daly M, Carsten OM (2014) Transition to manual: driver behaviour when resuming control from a highly automated vehicle. Transp Res Part F: Traffic Psychol Behav 27:274–282

    Article  Google Scholar 

  136. Rizk Y, Awad M, Tunstel EW (2018) Decision making in multiagent systems: a survey. IEEE Trans Cogn Dev Syst 10(3):514–529

    Article  Google Scholar 

Download references

Funding

This work is supported by the National Natural Science Foundation of China (72188101) and the National Social Science Foundation of China (20&ZD125).

Author information

Authors and Affiliations

Authors

Contributions

Nengying Chen performed the literature search, analysis, and wrote the manuscript under the supervision of Minglun Ren, who had the idea for the article. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Minglun Ren.

Ethics declarations

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Ethics Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed Consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, M., Chen, N. & Qiu, H. Human-machine Collaborative Decision-making: An Evolutionary Roadmap Based on Cognitive Intelligence. Int J of Soc Robotics 15, 1101–1114 (2023). https://doi.org/10.1007/s12369-023-01020-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-023-01020-1

Keywords

Navigation