Hybrid-augmented intelligence: collaboration and cognition

Nan-ning ZHENG; Zi-yi LIU; Peng-ju REN; Yong-qiang MA; Shi-tao CHEN; Si-yu YU; Jian-ru XUE; Ba-dong CHEN; Fei-yue WANG

doi:10.1631/FITEE.1700053

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Hybrid-augmented intelligence: collaboration and cognition

Special Feature on Artificial Intelligence 2.0 | Updated：2022-05-19

- Hybrid-augmented intelligence: collaboration and cognition
- 混合-增强智能：协作与认知
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 2, Pages: 153-179(2017)
- Affiliations：
  
  1Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China
  2National Engineering Laboratory of Visual Information Processing Applications, Xi'an Jiaotong University, Xi'an 710049, China
  3State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Author bio：
  
  E-mail:nnzheng@mail.xjtu.edu.cn
- Funds：
- DOI：10.1631/FITEE.1700053
  CLC： TP18
- Received：16 January 2017，
  
  Accepted：22 January 2017，
  
  Published：2017-02
- Accepted：
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Nan-ning ZHENG, Zi-yi LIU, Peng-ju REN, et al. Hybrid-augmented intelligence: collaboration and cognition[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 153-179.
DOI：

Nan-ning ZHENG, Zi-yi LIU, Peng-ju REN, et al. Hybrid-augmented intelligence: collaboration and cognition[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 153-179. DOI： 10.1631/FITEE.1700053.

摘要

人工智能追求的长期目标是使机器能像人一样学习和思考。由于人类面临的许多问题具有不确定性、脆弱性和开放性，任何智能程度的机器都无法完全取代人类，这就需要将人的作用或人的认知模型引入到人工智能系统中，形成混合-增强智能的形态，这种形态是人工智能或机器智能的可行的、重要的成长模式。混合-增强智能可以分为两类基本形式：一类是人在回路的人机协同混合增强智能，另一类是将认知模型嵌入机器学习系统中，形成基于认知计算的混合智能。本文讨论人机协同的混合-增强智能的基本框架，以及基于认知计算的混合-增强智能的基本要素：直觉推理与因果模型、记忆和知识演化；特别论述了直觉推理在复杂问题求解中的作用和基本原理，以及基于记忆与推理的视觉场景理解的认知学习网络；阐述了竞争-对抗式认知学习方法，并讨论了其在自动驾驶方面的应用；最后给出混合-增强智能在相关领域的典型应用。

Abstract

The long-term goal of artificial intelligence (AI) is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing

no matter how intelligent machines are

they are unable to completely replace humans. Therefore

it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI

that is

hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models: one is human-in-the-loop augmented intelligence with human-computer collaboration

and the other is cognitive computing based augmented intelligence

in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence

and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning

causal models

evolution of memory and knowledge

especially the role and basic principles of intuitive reasoning for complex problem solving

and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

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