CLC number: TP181
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-12-01
Cited: 0
Clicked: 3282
Citations: Bibtex RefMan EndNote GB/T7714
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.
@article{title="Parallel cognition: hybrid intelligence for human-machine interaction and management",
author="Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="12",
pages="1765-1779",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100335"
}
%0 Journal Article
%T Parallel cognition: hybrid intelligence for human-machine interaction and management
%A Peijun YE
%A Xiao WANG
%A Wenbo ZHENG
%A Qinglai WEI
%A Fei-Yue WANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 12
%P 1765-1779
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100335
TY - JOUR
T1 - Parallel cognition: hybrid intelligence for human-machine interaction and management
A1 - Peijun YE
A1 - Xiao WANG
A1 - Wenbo ZHENG
A1 - Qinglai WEI
A1 - Fei-Yue WANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 12
SP - 1765
EP - 1779
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100335
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 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.
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