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Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing

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dc.contributor.authorZeng, Yingyanen
dc.contributor.authorChen, Xiaoyuen
dc.contributor.authorJin, Ranen
dc.date.accessioned2023-02-07T20:09:27Zen
dc.date.available2023-02-07T20:09:27Zen
dc.date.issued2023-02en
dc.date.updated2023-02-07T19:50:55Zen
dc.description.abstractThe online sensing techniques and computational resources in an Industrial Cyber-physical System (ICPS) provide a digital foundation for data-driven decision making by artificial intelligence (AI) models. However, the poor data quality (e.g., inconsistent distribution, imbalanced classes) of high-speed, large-volume data streams poses significant challenges to the online deployment of the offline trained AI models. As an alternative, updating AI models online based on streaming data enables continuous improvement and resilient modeling performance. However, for a supervised learning model (i.e., a base learner), it is labor-intensive to continuously annotate all streaming samples and it is also challenging to select a subset with good quality to update the model. Hence, a data acquisition method is needed to select the data for annotation from streaming data to ensure data quality while saving annotation efforts. In the literature, active learning methods have been proposed to acquire informative samples. Different acquisition criteria were developed for exploration of under-represented regions in the input variable space or exploitation of the well-represented regions for optimal estimation of base learners. However, it remains a challenge to balance the exploration-exploitation trade-off under different online annotation scenarios. On the other hand, an acquisition criterion learned by AI (e.g., by reinforcement learning) adapts itself to a scenario dynamically, but the ambiguous consideration of the trade-off limits its performance in frequently changing manufacturing contexts. To overcome these limitations, we propose an ensemble active learning method by contextual bandits (CbeAL). CbeAL incorporates a set of active learning agents (i.e., acquisition criteria) explicitly designed for exploration or exploitation by a weighted combination of their acquisition decisions. The weight of each agent will be dynamically adjusted based on the usefulness of its decisions to improve the performance of the base learner. With adaptive and explicit consideration of both objectives, CbeAL efficiently guides the data acquisition process through selecting informative samples to reduce the human annotation efforts. Furthermore, we characterize the exploration and exploitation capability of the proposed agents theoretically. The evaluation results in a numerical simulation study and a real case study demonstrate the effectiveness and efficiency of CbeAL in manufacturing process modeling of the ICPS.en
dc.description.versionSubmitted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.orcidJin, Ran [0000-0003-3847-4538]en
dc.identifier.urihttp://hdl.handle.net/10919/113714en
dc.language.isoenen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectArtificial intelligenceen
dc.titleEnsemble Active Learning by Contextual Bandits for AI Incubation in Manufacturingen
dc.title.serialACM Transactions on Intelligent Systems and Technologyen
dc.typeArticleen
dc.type.dcmitypeTexten
dc.type.otherArticleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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