Neuro-Symbolic AI for Sensor-based Human Performance Prediction: System Architectures and Applications

Recently, due to the rapid development of deep learning methods, there has been a growing interest in Neuro-symbolic Artificial Intelligence, which takes advantage of both explicit symbolic knowledge and statistical sub-symbolic neural knowledge representations. In sensor-based human performance prediction (HPP) for safety-critical applications, where maintaining optimal human and system performance is a major concern, neuro-symbolic AI systems can improve sensor-based HPP tasks in complex working settings. In this paper, we focus on the advantages of hybrid neuro-symbolic AI systems, present the outstanding challenges and propose possible solutions for HPP in the safety-critical application domain.