平行认知：面向人机交互与管理的混合智能

叶佩军; 王晓; 郑文博; 魏庆来; 王飞跃

doi:10.1631/FITEE.2100335

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平行认知：面向人机交互与管理的混合智能

常规文章 | Updated：2022-12-16

- 平行认知：面向人机交互与管理的混合智能
  Enhanced Publication
- Parallel cognition: hybrid intelligence for human-machine interaction and management
- 信息与电子工程前沿（英文） 2022年23卷第12期页码：1765-1779
- Affiliations：
  
  1.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  2.Qingdao Academy of Intelligent Industries, Qingdao 266109, China
  3.School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China
  4.Macao Institute of System Engineering, Macau University of Science and Technology, Macau 999078, China
- Author bio：
  
  ‡Corresponding authors
- Funds：
  
  National Natural Science Foundation of China(62076237;62073321;U1811463);Youth Innovation Promotion Association, Chinese Academy of Sciences(2021130)
- DOI：10.1631/FITEE.2100335
  中图分类号： TP181
- 纸质出版日期：2022-12-0 ，
  
  网络出版日期：2022-08-04，
  
  收稿日期：2021-07-06，
  
  录用日期：2021-12-01
- Accepted：
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叶佩军, 王晓, 郑文博, 等. 平行认知：面向人机交互与管理的混合智能[J]. 信息与电子工程前沿（英文）, 2022,23(12):1765-1779.

PEIJUN YE, XIAO WANG, WENBO ZHENG, et al. Parallel cognition: hybrid intelligence for human-machine interaction and management. [J]. Frontiers of information technology & electronic engineering, 2022, 23(12): 1765-1779.
叶佩军, 王晓, 郑文博, 等. 平行认知：面向人机交互与管理的混合智能[J]. 信息与电子工程前沿（英文）, 2022,23(12):1765-1779. DOI： 10.1631/FITEE.2100335.

PEIJUN YE, XIAO WANG, WENBO ZHENG, et al. Parallel cognition: hybrid intelligence for human-machine interaction and management. [J]. Frontiers of information technology & electronic engineering, 2022, 23(12): 1765-1779. DOI： 10.1631/FITEE.2100335.

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。

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.

关键词

认知学习人工智能行为引导

Keywords

Cognitive learningArtificial intelligenceBehavioral prescription

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