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 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. Al-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. (C) 2022 CIRP. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a complementary learning paradigm to cultivate knowledge in a team of machines and humans, using lightweight neural networks and meta-learning. It designs AI-based teaming strategies to enable machines to leverage human expertise in decision-making, avoiding the need for complex sensor systems and expensive computation.