Parallel cognition: hybrid intelligence for human-machine interaction and management
Affiliation(s): State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Qingdao Academy of Intelligent Industries, Qingdao 266109, China; School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Macao Institute of System Engineering, Macau University of Science and Technology, Macau 999078, China; less
Received: 2021-07-06
Accepted: 2022-12-14
Available online: 2022-12-14
Abstract
As an interdisciplinary research approach, traditional cognitive science adopts mainly the experiment, induction, modeling, and validation paradigm. Such models are sometimes not applicable in cyber-physical-social-systems (CPSSs), where the large number of human users involves severe heterogeneity and dynamics. To reduce the decision-making conflicts between people and machines in human-centered systems, we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages: descriptive cognition based on artificial cognitive systems (ACSs), predictive cognition with computational deliberation experiments, and prescriptive cognition via parallel . To make iteration of these stages constantly on-line, a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual's cognitive knowledge. Preliminary experiments on two representative scenarios, urban travel and cognitive visual reasoning, indicate that our parallel cognition learning is effective and feasible for human , and can thus facilitate human-machine cooperation in both complex engineering and social systems.
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