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CLC number: TP181

On-line Access: 2022-12-14

Received: 2021-07-06

Revision Accepted: 2022-12-17

Crosschecked: 2021-12-01

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Citations:  Bibtex RefMan EndNote GB/T7714


Fei-Yue WANG


Peijun YE


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.12 P.1765-1779


Parallel cognition: hybrid intelligence for human-machine interaction and management

Author(s):  Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG

Affiliation(s):  State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; more

Corresponding email(s):   feiyue.wang@ia.ac.cn

Key Words:  Cognitive learning, Artificial intelligence, Behavioral prescription

Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG. Parallel cognition: hybrid intelligence for human-machine interaction and management[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1765-1779.

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publisher="Zhejiang University Press & Springer",

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T1 - Parallel cognition: hybrid intelligence for human-machine interaction and management
A1 - Peijun YE
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A1 - Fei-Yue WANG
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2100335

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 behavioral prescription. 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 behavioral prescription and cognitive visual reasoning, indicate that our parallel cognition learning is effective and feasible for human behavioral prescription, and can thus facilitate human-machine cooperation in both complex engineering and social systems.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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