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《信息与电子工程前沿(英文)》 >> 2022年 第23卷 第8期 doi: 10.1631/FITEE.2250000

人机混合增强智能:研究与应用

1西安交通大学人工智能与机器人研究所,中国西安市,710049;2兰州大学信息科学与工程学院,中国兰州市,730099;3普渡大学印第安纳波利斯分校工程技术学院电气与计算机工程系,美国印第安纳州印第安纳波利斯,46202-5195;4复旦大学计算机学院,中国上海市,200433

收稿日期: 2022-08-22 录用日期: 2022-08-22 发布日期: 2022-08-22

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摘要

Current research on artificial intelligence (AI) has been entering a new era, with AI technologies and AI-enabled applications emerging in almost every aspect of human life. Meanwhile, avoiding the risk caused by limitations of AI technologies has become a grand challenge. The main idea of human-machine augmented intelligence (HAI) is to adopt the role of humans or to embed human-like cognitive abilities into intelligent machines. Increasing attention and efforts from academia, industry, and governments are attracted by the HAI idea, whose effects are far-reaching. Two fundamental formulations of HAI include human-in-the-loop HAI (HITL-HAI) and cognitive computing based HAI (CC-HAI), which have become hot and fundamental frontiers of AI, and an increasing amount of original research has emerged in recent years. Recent existing research activities on HITL-HAI include theories for human-machine collaboration, human-brain interfaces, human-machine coordination and teaming, and advanced perception and smart environments for human-machine collaboration. In particular, HITL-HAI has been widely used in interactive simulation models in aviation, driving, and robotics. In such simulations, humans play an important role because they influence the simulated environment with their own actions. Brain-computer interfaces have become increasingly important among communication channels for human-machine collaboration. CC-HAI aims to develop computational models to mimic the mechanism or function of the human brain and improve a machine’s capabilities of perception, reasoning, and decision-making. We have witnessed an increasing amount of research work on casual models, intuitive reasoning models, and associative memories that are proposed with the forms of deep neural networks.

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