Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-26T17:06:55.263Z Has data issue: false hasContentIssue false
  • English
  • Français

ARE AI TOOLS GOING TO BE THE NEW DESIGNERS? A TAXONOMY FOR MEASURING THE LEVEL OF AUTOMATION OF DESIGN ACTIVITIES

Part of: Design Support Tools

Published online by Cambridge University Press:  11 June 2020

S. Altavilla  and
E. Blanco
S. Altavilla*
Affiliation:
Politecnico di Torino, Italy
E. Blanco
Affiliation:
Université Grenoble Alpes, France
*
*[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The digitalisation of the industry offers new opportunities to discuss design activities and support tools. Advancement in AI allows thinking about new Designer-AI tools interaction in the design process. The paper aims to initiate a characterisation of tools issued from researches in the application of AI in Design to rethink the division of work between Designer-AI tools. The paper is based on the literature on the concept of Levels of Automation in cognitive engineering, manufacturing and robotics, and proposes a grid of characterisation of the Level of Automation for the design process.

Keywords

design automationcomputational design methodsdesign support systemlevel of automation
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 81 - 90
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Billings, C.E. (1997), Aviation Automation: The Search for a Human-Centered Approach, Lawrence Erlbaum Associates, Mahwah, New Jersey, USA.Google Scholar
Bradshaw, J.M. et al. (2013), “The seven deadly myths of ‘autonomous systems’”, IEEE Intelligent Systems, Vol. 28 No. 3, pp. 54–61. https://doi.org/10.1109/MIS.2013.70CrossRefGoogle Scholar
Breton, R. and Bossé, É. (2003), “The cognitive costs and benefits of automation”, Defence Research and Development Canadavalcartier (Quebec).Google Scholar
Chiantella, N. (1982), “Achieving Integrated automation through computer networks”, SMA/CASA Computer Integrated Manufacturing Series, Vol. 1 No. 2, pp. 2–21.Google Scholar
Duncheon, C. (2002), “Product miniaturization requires automation – but with a strategy”, Assembly Automation, Vol. 22 No. 1, pp. 16–20.CrossRefGoogle Scholar
Endsley, M.R. and Kaber, D.B. (1999), “Level of automation effects on performance, situation awareness and workload in a dynamic control task”, Ergonomics, Vol. 42 No. 3, pp. 462–492.CrossRefGoogle Scholar
Finger, S. and Dixon, J.R. (1989), “A review of research in mechanical engineering design. Part I: Descriptive, prescriptive, and computer-based models of design processes”, Research in engineering design, Vol. 1 No. 1, pp. 51–67.CrossRefGoogle Scholar
Fitts, P.M. (1951), Human Engineering for an Effective Air-navigation and Traffic-control system, National Research Council, London.Google Scholar
Frohm, J. et al. (2008), “Levels of automation in manufacturing”, Ergonomia-an International journal of ergonomics and human factors, Vol. 30 No. 3.Google Scholar
Groover, M.P. (2001), Automation, Production Systems, and Computer-integrated Manufacturing, Prentice Hall, Upper Saddle River, N.J., USA.Google Scholar
Inagaki, T. (2003), “Adaptive automation: Sharing and trading of control”, Handbook of cognitive task design, Vol. 8, pp. 147–169.CrossRefGoogle Scholar
Johnson, M. et al. (2011), “Beyond cooperative robotics: The central role of interdependence in coactive design”, IEEE Intelligent Systems, Vol. 26 No 3, pp. 81–88. https://doi.org/10.1109/MIS.2011.47CrossRefGoogle Scholar
Kaber, D.B. (2018), “Issues in human–automation interaction modeling: Presumptive aspects of frameworks of types and levels of automation”, Journal of Cognitive Engineering and Decision Making, Vol. 12 No. 1, pp. 7–24.CrossRefGoogle Scholar
Karan, E. and Asadi, S. (2019), “Intelligent designer: A computational approach to automating design of windows in buildings”, Automation in Construction, Vol. 102, pp. 160–169.CrossRefGoogle Scholar
Miller, C.A. and Parasuraman, R. (2007), “Designing for flexible interaction between humans and automation: Delegation interfaces for supervisory control”, Human Factors, Vol. 49 No. 1, pp. 57–75. https://doi.org/10.1518/001872007779598037CrossRefGoogle ScholarPubMed
Mokammel, F. et al. (2018), “Automatic requirements extraction, analysis, and graph representation using an approach derived from computational linguistics”, Systems Engineering, Vol. 21 No. 6, pp. 555–575.CrossRefGoogle Scholar
Parasuraman, R., Sheridan, T.B. and Wickens, C.D. (2000), “A model for types and levels of human interaction with automation”, IEEE transactions on system, man, and cybernetics - Part A: Systems and humans, Vol. 30 No. 3, pp. 286–296.CrossRefGoogle Scholar
Proud, R.W., Hart, J.J. and Mrozinski, R.B. (2003), “Methods for determining the level of autonomy to design into a human spaceflight vehicle: a function specific approach”, National Aeronautics and Space Administration, Houston, TX, Lyndon B Johnson Space Center.Google Scholar
Ruff, H.A., Narayanan, S. and Draper, M.H. (2002), “Human interaction with levels of automation and decision-aid fidelity in the supervisory control of multiple simulated unmanned air vehicle”, Presence, Vol. 11 No. 4, pp. 335–351.Google Scholar
Save, L., Feuerberg, B. and Avia, E. (2012), “Designing human-automation interaction: a new level of automation taxonomy”, Proc. Human Factors of Systems and Technology, 2012.Google Scholar
Seidel, S. et al. (2018), “Autonomous tools and design: a triple-loop approach to human-machine learning”, Communications of the ACM, Vol. 62 No. 1, pp. 50–57.CrossRefGoogle Scholar
Sheridan, T.B. (1980), “Computer control and human alienation”, Technology Review, Vol. 83 No. 1, pp. 60–73.Google Scholar
Sheridan, T.B. and Verplank, W.L. (1978), “Human and computer control of undersea teleoperators”, Massachusetts Inst of Tech Cambridge Man-Machine Systems Lab.CrossRefGoogle Scholar
Srinivasan, V. and Chakrabarti, A. (2010), “An integrated model of designing”, Journal of Computing and Information Science in Engineering, Vol. 10 No. 3, p. 031013.CrossRefGoogle Scholar
Takeda, H., Veerkamp, P. and Yoshikawa, H. (1990), “Modeling design process”, AI magazine, Vol. 11 No. 4, pp. 37–37.Google Scholar
Vagia, M., Transeth, A.A. and Fjerdingen, S.A. (2016), “A literature review on the levels of automation during the years. What are the different taxonomies that have been proposed?”, Applied ergonomics, Vol. 53, pp. 190–202.CrossRefGoogle ScholarPubMed
Williams, T.J. (1999), “PERA and GERAM: Establishment of the place of the human in enterprise integration”, Proceedings of IFAC Congress, Beijing, China.CrossRefGoogle Scholar
Woods, D.D. (1985), “Cognitive technologies: The design of joint human-machine cognitive systems”, AI magazine, Vol. 6 No. 4, pp. 86–86.Google Scholar