Interactive Causality-Enabled Adaptive Machine Learning in Cyber-Physical Systems

Abstract

This chapter describes an adaptive machine learning (ML) method for the utilization of unlabeled data for continual model adaptation after deployment. Current methods for the usage of unlabeled data, such as unsupervised and semi-supervised methods, rely on being both smooth and static in their distributions. In this chapter, a generic method for leveraging causal relationships to automatically associate labels with unlabeled data using state transitions of asynchronous interacting cause and effect events is discussed. This self-labeling method is predicated on a defined causal relationship and associated temporal spacing. The theoretical foundation of the self-supervised method is discussed and compared with its contemporary semi-supervised counterparts using dynamical systems theory. Implementations of this method to adapt action recognition ML models in semiconductor manufacturing and human assembly tasks as manufacturing cyber-physical systems (CPS) are provided to demonstrate the effectiveness of the proposed methodology.