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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

Cited: 0

Clicked: 1198

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Fei-Yue WANG

https://orcid.org/0000-0001-9185-3989

Peijun YE

https://orcid.org/0000-0001-9987-9016

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

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


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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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.

平行认知:面向人机交互与管理的混合智能

叶佩军1,王晓1,2,郑文博3,魏庆来1,4,王飞跃1,2,4
1中国科学院自动化研究所复杂系统管理与控制国家重点实验室,中国北京市,100190
2青岛智能产业技术研究院,中国青岛市,266109
3西安交通大学软件学院,中国西安市,710049
4澳门科学与技术大学系统工程研究所,中国澳门特别行政区,999078
摘要:作为一门交叉学科,传统的认知科学主要采用实验、归纳、建模和验证的研究范式。对于包含大量用户异质行为和动态特性的社会物理信息系统,此种建模方法有时并不适用。为减少复杂人机系统中的人-机决策冲突,提出采用智能技术与系统来考察认知活动和认知功能的建模范式--平行认知。该范式分为三个阶段:基于人工认知系统的描述认知、基于计算思维实验的预测认知以及基于行为交互引导的引导性认知。在此基础上,进一步提出由心理模型和用户行为数据混合驱动的学习方法,自适应地学习人类个体的认知决策知识,从而使得三个阶段能够持续在线迭代。在交通行为引导和视觉推理场景下的初步实验表明,平行认知学习对于人类的行为引导是可行且有效的,有利于提升复杂工程系统和复杂社会系统中的人机协同程度。

关键词:认知学习;人工智能;行为引导

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

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