Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
