A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control

Abstract

The core theories, methods and technologies of contemporary system cognition, management, and control have been transferred to big data and artificial intelligence technology, resulting in a gap between the limitations of current artificial intelligence technology and the needs of complex system cognition, management, and control.As a result, a real need has spawned a new form of artificial intelligence: human-machine hybrid-augmented intelligence form, that is, the cooperation of human intelligence and machine intelligence runs through the process of system cognition, management, control, and so on.Human cognition and machine intelligence cognition are mixed together to form enhanced intelligence form.This form is a feasible and important growth mode of artificial intelligence or machine intelligence.A hybrid physics-data-knowledge (PDK) driven approach for human-machine hybrid-augmented intelligence-based system management and control was proposed.The proposed approach was illustrated by the following: trustworthy distributed data, computing, and algorithm, physics-informed deep learning, hybrid deep reinforce learning incorporating system operation rules, causal analysis, and interpretable AI and virtual digital human.In the context of power system dispatch and control, three examples were used for explaining the applications and technical pathways of the proposed PDK approach.

Keywords

• hybrid physics-data-knowledge driven approach;human-machine hybrid-augmented intelligence;system management and control;physics-informed deep learning;causal analysis;interpretable AI;virtual digital human