Complementary learning-team machines to enlighten and exploit human expertise

The benefits of Industry 4.0 are limited by the large computational requirements of ever-larger digital models of complex production systems. A complementary learning paradigm is thus proposed to cultivate knowledge in a team of machines and humans that represents the key to a high-performance manuf...

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Bibliographic Details
Published inCIRP annals Vol. 71; no. 1; pp. 417 - 420
Main Authors Li, Xingyu, Koren, Yoram, Epureanu, Bogdan I
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2022
Subjects
Artificial intelligence
Cognitive robotics
Human robot collaboration
Machine learning
Cognitive robotics
Artificial intelligence
Human robot collaboration
Machine learning
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Summary:The benefits of Industry 4.0 are limited by the large computational requirements of ever-larger digital models of complex production systems. A complementary learning paradigm is thus proposed to cultivate knowledge in a team of machines and humans that represents the key to a high-performance manufacturing system. Two types of knowledge are created using light-weighted neural networks and meta-learning: general knowledge of tasks and specific knowledge on collaboration with humans given few interactions. AI-based teaming strategies are designed to enable machines to leverage human expertise in making decisions using local communications that make intricate sensor systems and expensive computation unnecessary.
ISSN:0007-8506
DOI:10.1016/j.cirp.2022.04.019