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Imitating Design Cognition with Reinforcement Learning – Reflecting upon the Epistemology of Data Science
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0001-5118-4856
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
(English)In: Minds and Machines, ISSN 0924-6495, E-ISSN 1572-8641Article in journal (Refereed) Submitted
Abstract [en]

The complexity of (re)design of large-scale systems in a metropolitan context is challenging its designers and their design methods. Data science has the potential to influence the designing process, but the question is to what extent and in what way. In this paper, we present an approach with reinforcement learning to imitate design cognition in a use case on Electric Road Systems, by acting on information produced by data science. This takes place in a loose game that is an abstract representation of the real system, thus artificially producing knowledge. We further discuss the episteme of that knowledge produced by the artificial.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-259569OAI: oai:DiVA.org:kth-259569DiVA, id: diva2:1352183
Note

QC 20191113

Available from: 2019-09-17 Created: 2019-09-17 Last updated: 2019-11-13Bibliographically approved
In thesis
1. Creating Knowledge with Data Science for Design in Systems
Open this publication in new window or tab >>Creating Knowledge with Data Science for Design in Systems
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Designing in large-scale engineering systems is a difficult cognitive task undertaken by experts. Knowledge of experts continually changes as they are confronted with similar by different problems in designing in such systems. However, it is also important that designers are presented information that is representative of the system,and that they are cognizant of activities on a system scale so they can create diverse choices for designs in early phase of design process.Data Science has been proven to be effective at informing people for decisions at immediate horizons. But the use of data science to drive long terms designs where experts have to make the right series of decisions i.e. designs is yet unknown. The use of data science is to inform decision makers of activities at system scale.In this thesis, I have looked at how data science can be used to create knowledge in designers for designing in large scale systems. I have also investigated further questions regarding imitation of expertise using AI, and in generating similar knowledge by creating diverse options in design.The results point out that data science can indeed inform designers, change their designs and hence create knowledge. They also point out that design cognition in experts can be partly imitated in data science itself, through careful modeling of the ill-defined problem in design. This therefore points to a promising future direction where data can be used as an interface between human thinking and machine learning, by translation of conceptual forms such as differential diagnoses and cognitive artefacts using data.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2019. p. 33
Series
TRITA-CBH-FOU ; 46
Keywords
Data Science, Design Cognition, Transport, Healthcare, Artificial Intelligence, Imitation, Design Space Exploration
National Category
Computer Systems Health Sciences Transport Systems and Logistics
Research subject
Technology and Health
Identifiers
urn:nbn:se:kth:diva-259566 (URN)978-91-7873-299-9 (ISBN)
Public defence
2019-10-11, T2, Hälsovägen 11, 141 57, Huddinge, 09:30 (English)
Opponent
Supervisors
Note

QC 2019-09-20

Available from: 2019-09-20 Created: 2019-09-18 Last updated: 2019-10-18Bibliographically approved

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Shreenath, Vinutha Magal

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