Full Text:   <637>

CLC number: 

On-line Access: 2021-12-31

Received: 2021-07-06

Revision Accepted: 2021-12-01

Crosschecked: 0000-00-00

Cited: 0

Clicked: 1022

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2100335


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


Author(s):  Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Feiyue WANG

Affiliation(s):  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, Feiyue WANG. Parallel cognition: hybrid intelligence for human-machine interaction and managementy[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

@article{title="Parallel cognition: hybrid intelligence for human-machine interaction and managementy",
author="Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Feiyue WANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100335"
}

%0 Journal Article
%T Parallel cognition: hybrid intelligence for human-machine interaction and managementy
%A Peijun YE
%A Xiao WANG
%A Wenbo ZHENG
%A Qinglai WEI
%A Feiyue WANG
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100335

TY - JOUR
T1 - Parallel cognition: hybrid intelligence for human-machine interaction and managementy
A1 - Peijun YE
A1 - Xiao WANG
A1 - Wenbo ZHENG
A1 - Qinglai WEI
A1 - Feiyue WANG
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100335


Abstract: 
As an interdisciplinary research approach, traditional cognitive science mainly adopts the experiment, induction, modeling, and validation paradigm. Such models are probably 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 human participants and machines in human-centered systems, we propose a new research paradigm called parallel cognition that uses artificial intelligence (AI) techniques and has 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 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

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE