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混合-增强智能：协作与认知 Review
南宁 郑,子熠 刘,鹏举 任,永强 马,仕韬 陈,思雨 余,建儒 薛,霸东 陈,飞跃 王
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 153-179 doi: 10.1631/FITEE.1700053
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense Feature Article
Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu
Engineering 2020, Volume 6, Issue 3, Pages 310-345 doi: 10.1016/j.eng.2020.01.011
Recent progress in deep learning is essentially based on a “big data for small tasks” paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a “small data for big tasks” paradigm, wherein a single artificial intelligence (AI) system is challenged to develop “common sense,” enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of “why” and “how,” beyond the dominant “what” and “where” framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the “dark matter” of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace “dark” humanlike common sense for solving novel tasks.
Keywords： Computer vision Artificial intelligence Causality Intuitive physics Functionality Perceived intent Utility
“第四届中国认知计算与混合智能学术大会暨混合机器学习与自主系统论坛”在昆明举行
Conference Date: 18 Aug 2022
Conference Place: 中国
Administered by: 中国工程院
Visual knowledge: an attempt to explore machine creativity Perspectives
Yueting Zhuang, Siliang Tang,yzhuang@zju.edu.cn,siliang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2100116
Keywords： 思维科学；形象思维推理；视觉知识表达；视觉场景图
Parallel cognition: hybrid intelligence for human-machine interaction and management Research Article
Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12, Pages 1765-1779 doi: 10.1631/FITEE.2100335
Keywords： Cognitive learning Artificial intelligence Behavioral prescription
PASS - BDI Model for Software Agent
Fan Wei,Chen Zengqiang,Yuan Zhuzhi
Strategic Study of CAE 2004, Volume 6, Issue 6, Pages 43-49
Recent research on software agent is mainly based on rational agent theories that have been presented by Bratman and its core is to build BDI models for agent. But the models can not present the active cognitive processes of agent, and it is hard to richly present the relations between agent problem solving and agent mental states. Because it is not easy to build the explicit corresponding relations between the theory model and the model structure, agent rational models are difficult to realize. This paper introduces a psychologically recognized model-PASS (planning, attention, simultaneous processing and successive processing) into the study about intelligent agent, builds a new agent model named as PASS-BDI, describes the mental states, cognitive processes and whole behaviors with pi-calculus at length and strengthens the active cognitive attributes of agent. Because having built the explicit corresponding relations between this theory model and the model structure, it is easy to program in AOP practice. An application of the model in MAS is presented at last.
Keywords： agent pi-calculus cognitive processes mental state
Research on Development Strategy of Unmanned Driving Technology
Yang Yanming, Gao Zenggui, Zhang Zilong, Shen Yue, Wang Linjun
Strategic Study of CAE 2018, Volume 20, Issue 6, Pages 101-104 doi: 10.15302/J-SSCAE-2018.06.016
Unmanned driving technology is considered as a highly disruptive technology in the field of vehicle engineering. By means of expert interviews, comparative analysis and literature research, this paper analyses the development trend and stage of unmanned driving technology, and evaluates its potential values and social impacts. It also proposes to promote the development of unmanned driving technology by strengthening the research on automobile wire control technology, energy power technology and driving cognitive technology.
Keywords： disruptive technology unmanned driving technology automotive wire control technology energy power technology driving cognitive technology
Learning deep IA bidirectional intelligence Personal View
Lei XU
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 4, Pages 558-562 doi: 10.1631/FITEE.1900541
Keywords： Abstraction Least mean square error reconstruction (Lmser) Cognition Image thinking Abstract thinking Synthesis reasoning
Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning Research Article
Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG,lihq20@mails.tsinghua.edu.cn,huangjin@tsinghua.edu.cn,caoc15@mails.tsinghua.edu.cn,ydg@tsinghua.edu.cn
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1, Pages 131-140 doi: 10.1631/FITEE.2200128
Keywords： Pedestrian Hybrid reinforcement learning Autonomous vehicles Decision-making
Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing
Yang Bingru,Tang Jing
Strategic Study of CAE 2000, Volume 2, Issue 5, Pages 44-50
New framework of knowledge representation of fuzzy language field and fuzzy language value structure is shown in this paper. Then the generalized cell automation that can synthetically process fuzzy indeterminacy and random indeterminacy and the generalized inductive logic causal model are brought forward. On this basis, the new logic indeterminate causal inductive automatic reasoning mechanism which is based on fuzzy state describing is brought forward. At the end of this paper its application in the development of intelligent controller is discussed.
Keywords： language field language value structure generalized cell automation generalized inductive logic causal model automatic reasoning intelligent controller
Fei-Yue WANG, Jianbo GUO, Guangquan BU, Jun Jason ZHANG,jun.zhang.ee@whu.edu.cn
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8, Pages 1142-1157 doi: 10.1631/FITEE.2100418
Keywords： Complex systems Human-machine knowledge automation Parallel systems Bulk power grid dispatch Artificialintelligence Internet of Minds (IoM)
Towards the Unified Principles for Level 5 Autonomous Vehicles Article
Jianqiang Wang, Heye Huang, Keqiang Li, Jun Li
Engineering 2021, Volume 7, Issue 9, Pages 1313-1325 doi: 10.1016/j.eng.2020.10.018
The rapid advance of autonomous vehicles (AVs) has motivated new perspectives and potential challenges for existing modes of transportation. Currently, driving assistance systems of Level 3 and below have been widely produced, and several applications of Level 4 systems to specific situations have also been gradually developed. By improving the automation level and vehicle intelligence, these systems
can be further advanced towards fully autonomous driving. However, general development concepts for Level 5 AVs remain unclear, and the existing methods employed in the development processes of Levels 0–4 have been mainly based on task-driven function development related to specific scenarios. Therefore, it is difficult to identify the problems encountered by high-level AVs. The essential logical
and physical mechanisms of vehicles have hindered further progression towards Level 5 systems. By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving, we put forward a coordinated and balanced framework based on the brain–cerebellum–organ concept through reasoning and deduction. Based on a mixed mode relying on the crow inference and parrot imitation approach, we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning, self-adaptation, and self-transcendence for AVs. From a systematic, unified, and balanced point of view and based on least action principles and unified safety field concepts, we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs, specifically at Level 5.
Keywords： Autonomous vehicle Principle of least action Driving safety field Autonomous learning Basic paradigm
认知中继三跳网络联合优化 Article
澄 赵,万良 王,信威 姚,双华 杨
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 253-261 doi: 10.1631/FITEE.1601414
Keywords： 解码转发；三跳；认知中继网络；时间功率分配；叠加编码
Framework and case study of cognitive maintenance in Industry 4.0 Special Feature on Industrial Internet
Bao-rui Li, Yi Wang, Guo-hong Dai, Ke-sheng Wang,kesheng.wang@ntnu.no
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 11, Pages 1493-1504 doi: 10.1631/FITEE.1900193
Keywords： 认知维护；工业4.0；尖端设备；深度学习；绿色监视器；智能制造工厂
Causal Inference Review
Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang
Engineering 2020, Volume 6, Issue 3, Pages 253-263 doi: 10.1016/j.eng.2019.08.016
Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop eXplainable Artificial Intelligence (XAI) algorithms is one of key steps towards to the artificial intelligence 2.0. With the aim of bringing knowledge of causal inference to scholars of machine learning
and artificial intelligence, we invited researchers working on causal inference to write this survey from different aspects of causal inference. This survey includes the following sections: "Estimating average treatment effect: A brief review and beyond" from Dr. Kun Kuang, "Attribution problems in counterfactual inference" from Prof. Lian Li, "The Yule-Simpson paradox and the surrogate paradox" from Prof. Zhi Geng, "Causal potential theory" from Prof. Lei Xu, "Discovering causal information from observational data" from Prof. Kun Zhang, "Formal argumentation in causal reasoning and explanation" from Profs. Beishui Liao and Huaxin Huang, "Causal inference with complex experiments" from Prof. Peng Ding, "Instrumental variables and negative controls for observational studies" from Prof. Wang Miao, and "Causal inference with interference" from Dr. Zhichao Jiang.
Keywords： Causal inference Instructive variables Negative control Causal reasoning and explanation Causal discovery Counter factual inference Treatment effect estimation
Title Author Date Type Operation
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu
Journal Article
“第四届中国认知计算与混合智能学术大会暨混合机器学习与自主系统论坛”在昆明举行
18 Aug 2022
Conference Information
Visual knowledge: an attempt to explore machine creativity
Yueting Zhuang, Siliang Tang,yzhuang@zju.edu.cn,siliang@zju.edu.cn
Journal Article
Parallel cognition: hybrid intelligence for human-machine interaction and management
Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG
Journal Article
Research on Development Strategy of Unmanned Driving Technology
Yang Yanming, Gao Zenggui, Zhang Zilong, Shen Yue, Wang Linjun
Journal Article
Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning
Huiqian LI, Jin HUANG, Zhong CAO, Diange YANG, Zhihua ZHONG,lihq20@mails.tsinghua.edu.cn,huangjin@tsinghua.edu.cn,caoc15@mails.tsinghua.edu.cn,ydg@tsinghua.edu.cn
Journal Article
Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing
Yang Bingru,Tang Jing
Journal Article
Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems
Fei-Yue WANG, Jianbo GUO, Guangquan BU, Jun Jason ZHANG,jun.zhang.ee@whu.edu.cn
Journal Article
Towards the Unified Principles for Level 5 Autonomous Vehicles
Jianqiang Wang, Heye Huang, Keqiang Li, Jun Li
Journal Article
Framework and case study of cognitive maintenance in Industry 4.0
Bao-rui Li, Yi Wang, Guo-hong Dai, Ke-sheng Wang,kesheng.wang@ntnu.no
Journal Article
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