Towards improving automation with user input

As complex systems become more available, the possibility to leverage human intelligence to continuously train these systems is becoming increasingly valuable. Collecting and incorporating feedback from end-users into the system development processes could hold great potential for future development...

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Bibliographic Details
Main Author: Åström, Joakim
Format: Others
Language:English
Published: Linköpings universitet, Institutionen för datavetenskap 2021
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-180206
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spelling ndltd-UPSALLA1-oai-DiVA.org-liu-1802062021-10-15T05:24:25ZTowards improving automation with user inputengÅström, JoakimLinköpings universitet, Institutionen för datavetenskap2021feedbackinteractive machine learningactive machine learningsystem designsystem interactionautomationHuman Computer InteractionMänniska-datorinteraktion (interaktionsdesign)As complex systems become more available, the possibility to leverage human intelligence to continuously train these systems is becoming increasingly valuable. Collecting and incorporating feedback from end-users into the system development processes could hold great potential for future development of autonomous systems, but it is not without difficulties A literature review was conducted with the aim to review and help categorize the different dynamics relevant to the act of collecting and implementing user feedback in system development processes. Practical examples of such system are commonly found in active and interactive learning systems, which were studied with a particular interest towards possible novel applications in the industrial sector. This review was complimented by an exploratory experiment, aimed at testing how system accuracy affected the feedback provided by users for a simulated people recognition system. The findings from these studies indicate that when and how feedback is given along with the context of use is of importance for the interplay between system and user. The findings are discussed in relation to current directions in machine learning and interactive learning systems. The study concludes that factors such as system criticality, the phase in which feedback is given, how feedback is given, and the user’s understanding of the learning process all have a large impact on the interactions and outcomes of the user-automation interplay. Suggestions of how to design feedback collection for increased user engagement and increased data assimilation are given. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-180206application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic feedback
interactive machine learning
active machine learning
system design
system interaction
automation
Human Computer Interaction
Människa-datorinteraktion (interaktionsdesign)
spellingShingle feedback
interactive machine learning
active machine learning
system design
system interaction
automation
Human Computer Interaction
Människa-datorinteraktion (interaktionsdesign)
Åström, Joakim
Towards improving automation with user input
description As complex systems become more available, the possibility to leverage human intelligence to continuously train these systems is becoming increasingly valuable. Collecting and incorporating feedback from end-users into the system development processes could hold great potential for future development of autonomous systems, but it is not without difficulties A literature review was conducted with the aim to review and help categorize the different dynamics relevant to the act of collecting and implementing user feedback in system development processes. Practical examples of such system are commonly found in active and interactive learning systems, which were studied with a particular interest towards possible novel applications in the industrial sector. This review was complimented by an exploratory experiment, aimed at testing how system accuracy affected the feedback provided by users for a simulated people recognition system. The findings from these studies indicate that when and how feedback is given along with the context of use is of importance for the interplay between system and user. The findings are discussed in relation to current directions in machine learning and interactive learning systems. The study concludes that factors such as system criticality, the phase in which feedback is given, how feedback is given, and the user’s understanding of the learning process all have a large impact on the interactions and outcomes of the user-automation interplay. Suggestions of how to design feedback collection for increased user engagement and increased data assimilation are given.
author Åström, Joakim
author_facet Åström, Joakim
author_sort Åström, Joakim
title Towards improving automation with user input
title_short Towards improving automation with user input
title_full Towards improving automation with user input
title_fullStr Towards improving automation with user input
title_full_unstemmed Towards improving automation with user input
title_sort towards improving automation with user input
publisher Linköpings universitet, Institutionen för datavetenskap
publishDate 2021
url http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-180206
work_keys_str_mv AT astromjoakim towardsimprovingautomationwithuserinput
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