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A Novel DAO-Based Parallel Enterprise management Framework in Web3 Era

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第1期11卷 839-848页

作者： Wang, Ge Qin, Rui Li, Juanjuan Wang, Fei-Yue Gan, Yu Yan, Lihua Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Huituo Waytous Intelligent Robot Co Ltd Qingdao 266114 Peoples R China

This article proposes a novel parallel management mode based on decentralized autonomous organizations (DAOs) for enterprises by utilizing the artificial systems, computational experiments, parallel execution (ACP) approach, parallel intelligence theory, and blockchain technologies, to realize the distributed management of an enterprise. The artificial enterprise DAO (EnDAO) corresponding to the actual enterprise is constructed, and they constitute a parallel system via virtual-real interaction and parallel execution. Through the non-fungible token (NFT)-based incentive mechanism, metaverse-based virtual learning and training, as well as DAO-based distributed management and decision-making, the management and control of the actual enterprise as well as its employees can be carried out. By virtue of the virtual-real interactions of three types of employees, as well as the virtual-real feedback of three closed loops in the parallel systems, DAO-based parallel management for enterprises can realize descriptive intelligence, predictive intelligence, and prescriptive intelligence. On this basis, this article takes the recruitment-oriented key performance indicator (KPI) management of a startup technology enterprise as the case to introduce the operation processes and illustrate the superiorities of the proposed DAO-based enterprise parallel management mode.

关键词： Blockchain decentralized autonomous organizations (DAOs) metaverse parallel intelligence parallel management

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Approximate Dynamic Programming for Event-Driven H8 Constrained control

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第9期53卷 5922-5932页

作者： Yang, Xiong Xu, Mengmeng Wei, Qinglai Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Intelligent Unmanned Swarm Technol Tianjin 300072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

We study the dynamic event-driven Hop constrained control problem through approximate dynamic programming (ADP). Differing from the existing literature considering systems with either symmetric constraints or asymmetric constraints, we consider the two different constraints simultaneously. Initially, by constructing a generalized nonquadratic value function, we trans -form the H-8 constrained control problem into an unconstrained two-player zero-sum game. Then, we present an event-driven Hamilton-Jacobi-Isaacs equation (ED-HJIE) corresponding to the zero-sum game for lowering down the computational load. To solve the ED-HJIE, we propose a dynamic triggering mech-anism together with a sole critic neural network (CNN) being built under the ADP framework. The CNN's weights are tuned via the gradient descent approach. After that, we prove uniform ultimate boundedness of the closed-loop system and the CNN's weight estimation error via Lyapunov's method. Finally, we sepa-rately use an F16 aircraft plant and an inverted pendulum system to validate the present theoretical claims.

关键词： Approximate dynamic programming (ADP) event-driven control H(8)control input constraint optimal control

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Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 5284-5297页

作者： Gao, Junyu Ma, Xinhong Xu, Changsheng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst MA Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Peng Cheng Lab Shenzhen Peoples R China

Despite the great progress of unsupervised domain adaptation (UDA) with the deep neural networks, current UDA models are opaque and cannot provide promising explanations, limiting their applications in the scenarios that require safe and controllable model decisions. At present, a surge of work focuses on designing deep interpretable methods with adequate data annotations and only a few methods consider the distributional shift problem. Most existing interpretable UDA methods are post-hoc ones, which cannot facilitate the model learning process for performance enhancement. In this paper, we propose an inherently interpretable method, named Transferable Conceptual Prototype Learning (TCPL), which could simultaneously interpret and improve the processes of knowledge transfer and decision-making in UDA. To achieve this goal, we design a hierarchically prototypical module that transfers categorical basic concepts from the source domain to the target domain and learns domain-shared prototypes for explaining the underlying reasoning process. With the learned transferable prototypes, a self-predictive consistent pseudo-label strategy that fuses confidence, predictions, and prototype information, is designed for selecting suitable target samples for pseudo annotations and gradually narrowing down the domain gap. Comprehensive experiments show that the proposed method can not only provide effective and intuitive explanations but also outperform previous state-of-the-arts. Code is available at https://***/file/d/1b1EHFghiF1ExD-Cn1HYg75VutfkXWp60/view?usp=sharing.

关键词： Prototypes Adaptation models Motorcycles Image recognition Decision making Cognition Target recognition Explainable AI transfer learning image classification deep learning

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Urban Traffic control Meets Decision Recommendation system:A Survey and Perspective

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2043-2058页

作者： Qingyuan Ji Xiaoyue Wen Junchen Jin Yongdong Zhu Yisheng Lv Zhejiang Lab Hangzhou 311121China College of Engineering and Architecture Zhejiang UniversityHangzhou 310013China College of Electrical Engineering Zhejiang UniversityHangzhou 310013China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation *** approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and ***,this process is cumbersome,labor-intensive,and cannot be applied on a large network *** studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and *** a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been *** this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.

关键词： Recommendation system traffic control traffic perception traffic prediction

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Dynamic-Horizon Model-Based Value Estimation With Latent Imagination

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2024年第7期35卷 8812-8825页

作者： Wang, Junjie Zhang, Qichao Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

Existing model-based value expansion (MVE) methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, a proper horizon setting is essential to world-model-based policy learning. Meanwhile, choosing an appropriate horizon value is time-consuming, especially for visual control tasks. In this article, we investigate the idea of adaptively using the model knowledge for value expansion. We propose a novel world-model-based method called dynamic-horizon MVE (DMVE) to adjust the use of the world model with adaptive rollout horizon selection. Based on the reconstruction-based technique, the raw and reconstructed images are both used to obtain multihorizon rollouts by utilizing latent imagination. Then, a horizon reliability degree detection approach is given to select appropriate horizons and obtain more accurate value estimation by the reconstructed value expansion errors. Experimental results on the mainstream benchmark visual control tasks show that DMVE outperforms all baselines in sample efficiency and final performance. In addition, experiments on the autonomous driving lane-changing task further demonstrate the scalability of our method. The codes of DMVE are available at https://***/JunjieWang95/dmve.

关键词： Task analysis Adaptation models Image reconstruction Visualization Estimation Heuristic algorithms Computational modeling Latent world model model-based value expansion (MVE) reinforcement learning rollout horizon

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ArtVerse: A Paradigm for Parallel Human-Machine Collaborative Painting Creation in Metaverses

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第4期53卷 2200-2208页

作者： Guo, Chao Dou, Yong Bai, Tianxiang Dai, Xingyuan Wang, Chunfa Wen, Yi Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Natl Museum China Beijing 100006 Peoples R China Natl Museum China Inst Sci & Art Beijing 100006 Peoples R China

Currently, the development of the foundation model, metaverse, nonfungible token (NFT), and other emerging technologies has brought profound effects on the whole art field, including art creation, dissemination, transaction, etc. However, there is no research focusing on the framework, methodologies, and applications of the human-machine collaborative creation in the metaverse era. Based on parallel theory, this article proposes a novel human-machine collaborative creation paradigm called ArtVerse, in which machines take on the roles of humans to perform creation exploration and evolution and build decentralized art organizations. Besides, the operational processes involving several key technologies are designed to achieve the proposed ArtVerse. Then, a prototype system of ArtVerse, our long-term efforts toward the human-machine collaborative painting, is presented. Finally, a new ecology of artistic creation in the metaverse era is demonstrated through the applications of the ArtVerse.

关键词： Artificial intelligence (AI) art artificial intelligence generated content (AIGC) parallel theory co-creation cyber-physical-social system (CPSS) decentralized/distributed autonomous operations and organizations (DAOs) foundation model human-machine collaboration metaverse robotic painting

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Toward Sustainable Intelligent Transportation systems in 2050: Fairness and Eco-Responsibility

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第6期8卷 3537-3540页

作者： Chen, Jicheng Tao, Siyou Teng, Siyu Chen, Yuanyuan Zhang, Hui Wang, Fei-Yue Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China

The Paris Agreement set global energy conservation and emission reduction standards in 2016, and the ambitious goal of carbon neutrality must be achieved by 2050. However, the conflicts between environmental protection, economic development and social fairness constrain the development of sustainable transportation systems. At the same time, the automotive industry believes that large-scale application of autonomous driving technology is expected by 2050. The development and application of autonomous driving technology can provide a new solution to improve the sustainability of the transportation system. Meanwhile, autonomous vehicles can significantly enhance the mobility of people with disabilities and seniors and reduce the transportation cost for poverty people, improving the transportation fairness of the entire society. This letter discusses the impact of autonomous driving technology on sustainable transportation systems from the perspectives of social fairness and economic responsibility, as a brief summary of the third part of Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL).

关键词： Sustainability autonomous vehicles social fairness carbon neutrality green transportation

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Autogeneration of Mission-Oriented Robot controllers Using Bayesian-Based Koopman Operator

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 903-918页

作者： Pan, Jie Li, Dongyue Wang, Jian Zhang, Pengfei Shao, Jinyan Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Nanchang Innovat Inst Nanchang 330096 Peoples R China

Model-based robot controllers require customized control-oriented models, involving expert knowledge and trial and error. Remarkably, the Koopman operator enables the control-oriented model identification through the input-output mapping set, breaking through the barriers of the customization services. However, in recent years, research on Koopman-based robot control has mostly focused on lifting function construction, deviating from the original intention of improving the controller performance. Thus, we propose a robot controller autogeneration framework using the Bayesian-based Koopman operator, significantly releasing labor and eliminating the design obstacle. First, we introduce the Koopman-based system identification method and offer the basic lifting function design criteria. Then, a Bayesian-based optimization strategy with resource allocation is designed, which allows for the simultaneous optimization of the lifting function and the controller. Next, taking model-predictive control (MPC) as an example, a mission-oriented controller autogeneration framework is developed. Simulation and experimental results indicate that, under various robots and data sources, the proposed framework can effectively generate the robot controllers and perform with a far greater level of mission accuracy than the unoptimized Koopman-based MPC. Meanwhile, the proposed technique exhibits an obvious compensation effect against disturbances, demonstrating its practicability in robot control.

关键词： Bayesian optimization (BO) control-oriented model identification Koopman operator robot control

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VirtualClassroom: A Lecturer-Centered Consumer-Grade Immersive Teaching system in Cyber-Physical-Social Space

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第6期53卷 3501-3513页

作者： Shen, Tianyu Huang, Shi-Sheng Li, Deqi Lu, Zhiyuan Wang, Fei-Yue Huang, Hua Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Lecturers, as the guidance of the classroom, play a significant role in the teaching process. However, the lecturers' sense of space immersion has been ignored in current virtual teaching systems. In this article, we explore the cyber-physical-social intelligence for Edu-Metaverse in cyber-physical-social space and specially design a lecturer-centered immersive teaching system, taking the social and lecturers' factors into consideration. We call this system VirtualClassroom (V-Classroom). Specifically, we first introduce the cyber-physical-social system (CPSS) paradigm of V-Classroom so that the workflow is standardized and significantly simplified, and the systems can be constructed with off-the-shelf hardware. The key component of V-Classroom is a cyber-world representation of a physical-world classroom instrumented with sparse consumer-grade RGBD cameras for capturing the 3-D geometry and texture of the classrooms. We provide each V-Classroom lecturer with a physical device for sending 6DoF view-change messages and showing view-dependent content of the remote classroom. Following the above paradigm, we develop the V-Classroom algorithms, including V-Classroom depth algorithm (V-DA) and V-Classroom view algorithm (V-VA), to achieve the real-time rendering of remote classrooms. V-DA is dedicated to recovering accurate depth information of the classrooms while V-VA is devoted to real-time novel view synthesis. Finally, we illustrate our implemented CPSS-driven V-Classroom prototype, based on real-world classroom scenarios we collected, and discuss the main challenges and future direction.

关键词： 6DoF video cyber-physical-social systems (CPSSs) educational metaverse immersive teaching

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Adaptive Dynamic Programming for Nonlinear-Constrained H8 control

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第7期53卷 4393-4403页

作者： Yang, Xiong Xu, Mengmeng Wei, Qinglai Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article considers the H-infinity control problem of nonlinear systems having unavailable dynamics and asymmetric saturating actuators. Initially, such an H-infinity control problem is converted into the zero-sum game with a nonquadratic cost function being introduced. Then, in order to solve the Hamilton-Jacobi-Isaacs equation arising in the zero-sum game, a simultaneous policy iteration (SPI) algorithm is developed under the adaptive dynamic programming framework. Meanwhile, it is proved that the convergence of the SPI algorithm in essence amounts to the convergence of the sequential PI algorithm. To implement the SPI algorithm, the critic, the actor, and the perturbation neural networks (NNs) are, respectively, constructed to estimate the cost function, the control policy, and the perturbation. The three NNs' weights are simultaneously determined by using the least-squares method together with the Monte Carlo integration technique. A remarkable characteristic of such an SPI algorithm is that arbitrary control policies and perturbations are applicable in the learning process. This makes system's information be able to be replaced by the data collected along system's trajectories in advance. More importantly, the persistence of the excitation condition is not required. Finally, simulations of two nonlinear examples are given to validate the present SPI algorithm.

关键词： Perturbation methods Games Game theory Nonlinear systems Heuristic algorithms Artificial neural networks Dynamic programming Adaptive approximate dynamic programming (ADP) asymmetric constraint neural network (NN) control simultaneous policy iteration (SPI)

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