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Specifying autonomy in the Internet of Things: the autonomy model and notation

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Abstract

Driven by digitization in society and industry, automating behavior in an autonomous way substantially alters industrial value chains in the smart service world. As processes are enhanced with sensor and actuator technology, they become digitally interconnected and merge into an Internet of Things (IoT) to form cyber-physical systems. Using these automated systems, enterprises can improve the performance and quality of their operations. However, currently it is neither feasible nor reasonable to equip any machine with full autonomy when networking with other machines or people. It is necessary to specify rules for machine behavior that also determine an adequate degree of autonomy to realize the potential benefits of the IoT. Yet, there is a lack of methodologies and guidelines to support the design and implementation of machines as explicit autonomous agents such that many designs only consider autonomy implicitly. To address this research gap, we perform a comprehensive literature review to extract 12 requirements for the design of autonomous agents in the IoT. We introduce a set of constitutive characteristics for agents and introduce a classification framework for interactions in multi-agent systems. We integrate our findings by developing a conceptual modeling language consisting of a meta model and a notation that facilitates the specification and design of autonomous agents within the IoT as well as CPS: the autonomy model and notation. We illustrate and discuss the approach and its limitations.

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References

  1. Agah A (2000) Human interactions with intelligent systems: research taxonomy. Comput Electr Eng 27(1):71–107

  2. Anderson M, Anderson SL (eds) (2011) Machine ethics. Cambridge University Press, Cambridge

  3. Apfelbacher R, Rozinat A (2003) Fundamental modeling concepts (FMC) notation reference. http://www.fmc-modeling.org/download/notation_reference/FMC-Notation_Reference.pdf. Accessed 11 May 2018

  4. Ashton K (2009) That “Internet of Things” thing. RFID J. https://www.rfidjournal.com/articles/view?4986. Accessed 11 May 2018

  5. Barros A, Oberle D (eds) (2012) Handbook of service description: USDL and its methods. Springer, New York

  6. Beverungen D, Matzner M, Janiesch C (2017) Information systems for smart services. ISeB 15(4):781–787

  7. Bitsaki M, Danylevych O, van den Heuvel W, Koutras G, Leymann F, Mancioppi M, Nikolaou C, Papazoglou M (2008) An architecture for managing the lifecycle of business goals for partners in a service network. In: Mähönen P, Pohl K, Priol T (eds) 1st European conference service wave. Lecture Notes in computer science, Madrid, pp 196–207

  8. Bradshaw J, Feltovich P, Jung H, Kulkarni S, Uszok WTA (2004) Dimensions of adjustable autonomy and mixed-initiative interaction. In: Agents and computational autonomy. LNCS, vol 2969, pp 17–39

  9. Bresciani P, Giorgini P, Giunchiglia F, Mylopoulos J, Perini A (2004) TROPOS: an agent-oriented software development methodology. Auton Agent Multi Agent Syst 2(3):203–236

  10. Brustoloni J (1991) Autonomous agents: characterization and requirements. Technical report. Carnegie Mellon University, Pittsburgh, PA

  11. Cardoso J (2013) Modeling service relationships for service networks. In: Falcão e Cunha J, Snene M, Nóvoa H (eds) 3rd international conference on exploring services sciences (IESS). Lecture notes in business information processing, vol Porto. Springer, pp 114–128

  12. Cardoso J, Pedrinaci C, Leenheer P (2013) Open semantic service networks: modeling and analysis. In: Falcão e Cunha J, Snene M, Nóvoa H (eds) 3rd international conference on exploring services sciences (IESS). Lecture notes in business information processing, vol Porto. Springer, pp 141–154

  13. Castelfranchi C (2000) Founding agent’s ‘autonomy’ on dependence theory. In: 14th European conference on artificial intelligence, Amsterdam, pp 353–357

  14. Cervenka R, Trencansky I (2000) The agent modeling language—AML. Birkhäuser, Basel

  15. Chen PP-S (1976) The entity relationship model: toward a unified view of data. ACM Trans Database Syst 1(1):9–36

  16. Chen PP (2003) Toward a methodology of graphical icon design. In: 5th IEEE international symposium on multimedia software engineering (ISMSE), Taichung, pp 120–121

  17. Chestnut H (1963) Automation: what it is and what are the problems it poses. Automatica 1(4):241–252

  18. Danylevych O, Karastoyanova D, Leymann F (2010) Service networks modelling: an SOA & BPM standpoint. J Univ Comput Sci 16(13):1668–1693

  19. Delfmann P (2006) Adaptive Referenzmodellierung: Methodische Konzepte zur Konstruktion und Anwendung wieder verwendungsorientierter Informationsmodelle. Dissertation, Logos, Berlin

  20. Dresner K, Stone P (2008) A multiagent approach to autonomous: intersection management. J Artif Intell Res 31(1):591–656

  21. Endsley M (1987) The application of human factors to the development of expert systems for advanced cockpits. In: Human factors and ergonomics society 31st annual meeting, New York, NY, pp 1388–1392

  22. Endsley M, Kaber D (1999) Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics 42(3):162–192

  23. Endsley M, Kiris E (1995) The out-of-the-loop performance problem and level of control in automation. J Hum Factors Ergon Soc 37(2):381–394

  24. Etzion O, Niblett P (2010) Event processing in action. Manning Publications, Cincinnati

  25. Evans R, Kearny P, Stark J, Caire G, Garijo F, Sanz GJ, Leal F, Chainho P, Massonet P (2001) MESSAGE: methodology for engineering systems of software agents. Methodology for agent-oriented software engineering. Technical Report Eurescom project P907, EDIN 0223-0907, EURESCOM. http://www.upv.es/sma/teoria/metodologias/articulos/D3finalReviewed.pdf. Accessed 05 Sept 2018

  26. Franklin S, Graesser A (1997) Is it an agent, or just a program? A taxonomy for autonomous agents. In: Intelligent agents III: agent theories, architectures and languages. LNCS vol 1193. Springer, Berlin, pp 21–35

  27. Geisberger E, Broy M (2012) agendaCPS: Integrierte Forschungsagenda cyber-physical systems. Springer, Heidelberg

  28. Gill H (2008) From vision to reality: cyber-physical systems. In: HCSS national workshop on new research directions for high confidence transportation CPS: automotive, aviation, and rail, Tyson’s Corner, VA

  29. Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Q 37(2):337–355

  30. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gen Comput Syst 29(7):1645–1660

  31. Harmsen AF (1997) Situational method engineering. Dissertation, Moret Ernst & Young Management Consultants, Utrecht

  32. Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75–105

  33. Holland JH (2006) Studying complex adaptive systems. J Syst Sci Complex 19(1):1–8

  34. Jacobson I, Booch G, Rumbaugh J (1999) The unified software development process. Addison-Wesley Professional, Reading

  35. Janiesch C (2007) Contextual method design: constructing adaptable modeling methods for information systems development. Dissertation, Münster

  36. Jennings N (1999) Agent-based Computing: promise and perils. In: 16th international joint conference on artificial intelligence, Stockholm, pp 1429–1436

  37. Jipp M (2014) Levels of automation: effects of individual differences on wheelchair control performance and user acceptance. Theor Issues Ergon Sci 15(5):479–504

  38. Kalenka S, Jennings N (1999) Socially responsible decision making by autonomous agents. In: 5th international colloquium on cognitive science, Donostia-San Sebastián. Springer, pp 135–149

  39. Karlsson F (2005) Method configuration: method and computerized tool support. Dissertation, Linköping

  40. Kartseva V, Hulstijn J, Gordijn J, Tan Y-H (2010) Control patterns in a health-care network. Eur J Inf Syst 19(3):320–343. https://doi.org/10.1057/ejis.2010.13

  41. Lasi H, Fettke P, Feld T, Hoffmann M (2014) Industry 4.0. Bus Inf Syst Eng 6(4):239–242

  42. Lee EA (2008) Cyber physical systems: design challenges. Technical report no. UCB/EECS-2008-8. University of California, Berkeley, CA

  43. Lee J, See K (2004) Trust in automation: designing for appropriate reliance. J Hum Factors Ergon Soc 46(1):50–80

  44. March TS, Smith G (1995) Design and natural science research on information technology. Decis Support Syst 15(4):251–266

  45. Moody DL (2009) The “physics” of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans Softw Eng 35(6):756–779

  46. Moor J (2006) The nature, importance, and difficulty of machine ethics. IEEE Intell Syst 21(4):18–21

  47. Munroe S, Luck M (2003) Agent autonomy through the 3M motivational taxonomy. In: 1st international workshop on computational autonomy (AUTONOMY). LNCS vol 2969. Springer, New York, NY, pp 55–67

  48. National Science Foundation (2016) Partnerships for innovation: building innovation capacity (PFI:BIC). Program solicitation NSF16-591. https://www.nsf.gov/pubs/2016/nsf16591/nsf16591.pdf. Accessed 19 Sept 2017

  49. Object Management Group, Inc. (2013) Business process model and notation (BPMN). Version 2.0.2. http://www.omg.org/spec/BPMN/2.0.2/PDF. Accessed 05 Sept 2018

  50. Object Management Group, Inc. (2016a) Case management model and notation (CMMN). Version 1.1. http://www.omg.org/spec/CMMN/1.1/PDF/. Accessed 14 Sept 2017

  51. Object Management Group, Inc. (2016b) Decision model and notation (DMN). Version 1.1. https://www.omg.org/spec/DMN/1.1/PDF. Accessed 05 Sept 2018

  52. Odell J, van Dyke Parunak H, Bauer B (2000) Extending UML for agents. In: 2nd international CAiSE workshop on agent-oriented information systems (AOIS), Stockholm. ICue, pp 1–15

  53. Onnasch L, Wickens C, Li H, Manzey D (2014) Human performance consequences of stages and levels of automation: an integrated meta-analysis. J Hum Factors Ergon Soc 56(3):476–488

  54. Parasuraman R, Riley V (1997) Humans and automation: use, misuse, disuse, abuse. J Hum Factors Ergon Soc 39(2):230–253

  55. Parasuraman R, Wickens C (2008) Humans: still vital after all these years of automation. J Hum Factors Ergon Soc 50(3):511–520

  56. Parasuraman R, Sheridan T, Wickens C (2000) A model for types and levels of human interaction with automation. IEEE Trans Syst Man Cybern Part A Syst Hum 30(3):286–297

  57. Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S (2007) A design science research methodology for information systems research. J Manag Inf Syst 24(3):45–77

  58. Pereira LM, Saptawijaya A (2016) Programming machine ethics. Springer, Basel

  59. Recker J, Reijers HA, van de Wouw SG (2010) An integrative framework of the factors affecting process model understanding: a learning perspective. In: Leidner D, Elam J (eds) 16th Americas conference on information systems (AMCIS), Lima, pp 5087–5097

  60. Ren K, Samarati P, Gruteser M, Ning P, Liu Y (2014) Guest editorial: special issue on security for IoT: the state of the art. IEEE Internet Things J 1(5):369–371

  61. Richling J, Werner M, Jaeger M, Mühl G, Heiß H-U (2011) Autonomie in verteilten IT-Architekturen. De Gruyter, München

  62. Russell S, Norvig P (2014) Articial intelligence: a modern approach. Pearson, Harlow

  63. Schillo M, Fischer K (2003) A taxonomy of autonomy in multi-agent organisation. In: 1st international workshop on computational autonomy (AUTONOMY). Lecture notes in computer science, Melbourne. Springer, pp 68–82

  64. Schütte R, Rotthowe T (1998) The guidelines of modeling: an approach to enhance the quality in information models. In: Ling TW, Ram S, Lee ML (eds) 17th international conference on conceptual modeling (ER). Lecture notes in computer science, Singapore, pp 240–254

  65. Sheridan T (1997) Supervisory Control. In: Salvendy G (ed) Handbook of human factors. Wiley, New York, pp 1025–1052

  66. Sheridan T (2011) Adaptive automation, level of automation, allocation authority, supervisory control, and adaptive control: distinctions and modes of adaptation. IEEE Trans Syst Man Cybern Part A Syst Hum 41(4):662–667

  67. Sheridan T, Parasuraman R (2005) Human–automation interaction. Rev Hum Factors Ergon 1(1):89–129

  68. Sheridan T, Verplank W (1978) Human and computer control of undersea teleoperators. In: Technical report. Massachusetts Institute of Technology, Cambridge, MA

  69. Silva VT, Choren R, Lucena C (2004) A UML based approach for modeling and implementing multi-agent systems. In: 3rd international joint conference on autonomous agents and multiagent systems (AAMAS), New York, NY, pp 914–921

  70. The Open Group (2017) ArchiMate® 3.0.1 specification. http://pubs.opengroup.org/architecture/archimate3-doc/. Accessed 11 May 2018

  71. Wagner G (2003) The agent-object-relationship meta-model: towards a unified conceptual view of state and behavior. Inf Syst 28(5):475–504

  72. Weiser M (1991) The computer for the 21st century. Sci Am 265(3):94–104

  73. Wickens CD, Hollands J (2000) Engineering psychology and human performance, 3rd edn. Prentice Hall, Upper Saddle River

  74. Wooldridge M, Jennings N (1995) Intelligent agents: theory and practice. Knowl Eng Rev 10(2):115–152

  75. Wooldridge M, Jennings NR, Kinny D (2000) The Gaia methodology for agent-oriented analysis and design. J Auton Agents Multi Agent Syst 3(3):285–312

  76. Yanco H, Drury J (2004) Classifying human–robot interaction: an updated taxonomy. In: IEEE international conference on systems, man and cybernetics, The Hague, pp 2841–2846

  77. Zhou W, Jia Y, Peng A, Zhang Y, Liu P (2018) The effect of IoT new features on security and privacy: new threats, existing solutions, and challenges yet to be solved. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2018.2847733

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Correspondence to Christian Janiesch.

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Janiesch, C., Fischer, M., Winkelmann, A. et al. Specifying autonomy in the Internet of Things: the autonomy model and notation. Inf Syst E-Bus Manage 17, 159–194 (2019). https://doi.org/10.1007/s10257-018-0379-x

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Keywords

  • Autonomy
  • Agent
  • Internet of Things
  • Cyber-physical systems
  • Conceptual modeling language
  • Notation