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Title: Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition-Based Maintenance Planning and Visualisation
Authors: Ribeiro, Jorge 
Andrade, Pedro 
Carvalho, Manuel Costa 
Silva, Catarina 
Ribeiro, Bernardete 
Roque, Licínio 
Keywords: design; remaining useful life; visualisation; machine learning; reinforcement learning; condition based maintenance; aircraft maintenance planning
Issue Date: 2022
Project: info:eu-repo/grantAgreement/EC/H2020/769288/EU 
Serial title, monograph or event: Mathematics
Volume: 10
Issue: 9
Abstract: Aircraft maintenance is a complex domain where designing new systems that include Machine Learning (ML) algorithms can become a challenge. In the context of designing a tool for Condition-Based Maintenance (CBM) in aircraft maintenance planning, this case study addresses (1) the use of Playful Probing approach to obtain insights that allow understanding of how to design for interaction with ML algorithms, (2) the integration of a Reinforcement Learning (RL) agent for Human–AI collaboration in maintenance planning and (3) the visualisation of CBM indicators. Using a design science research approach, we designed a Playful Probe protocol and materials, and evaluated results by running a participatory design workshop. Our main contribution is to show how to elicit ideas for integration of maintenance planning practices with ML estimation tools and the RL agent. Through a participatory design workshop with participants’ observation, in which they played with CBM artefacts, Playful Probes favour the elicitation of user interaction requirements with the RL planning agent to aid the planner to obtain a reliable maintenance plan and turn possible to understand how to represent CBM indicators and visualise them through a trajectory prediction.
ISSN: 2227-7390
DOI: 10.3390/math10091604
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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