Resource Type

Journal Article 1390

Conference Videos 208

Conference Information 98

Conference Topics 3

Year

2024 4

2023 155

2022 200

2021 235

2020 218

2019 176

2018 103

2017 89

2016 42

2015 36

2014 25

2013 19

2012 20

2011 27

2010 26

2009 30

2008 24

2007 41

2006 44

2005 36

open ︾

Keywords

Machine learning 42

Artificial intelligence 35

Deep learning 34

intelligent manufacturing 20

artificial intelligence 18

Reinforcement learning 14

Intelligent manufacturing 8

Multi-agent system 8

Additive manufacturing 6

Multi-agent systems 6

industrial software 6

Big data 5

Neural network 5

Smart manufacturing 5

intelligence 5

Process industry 4

Process intensification 4

control 4

engineering 4

open ︾

Search scope:

排序： Display mode:

Prospects for multi-agent collaboration and gaming: challenge, technology, and application Perspective

Yu LIU, Zhi LI, Zhizhuo JIANG, You HE

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1002-1009 doi: 10.1631/FITEE.2200055

Abstract: Recent years have witnessed significant improvement of multi-agent systems for solving various decision-making problems in complex environments and achievement of similar or even better performance than humans. In this study, we briefly review multi-agent collaboration and gaming technology from three perspectives, i.e., task challenges, technology directions, and application areas. We first highlight the typical research problems and challenges in the recent work on multi-agent systems. Then we discuss some of the promising research directions on multi-agent collaboration and gaming tasks. Finally, we provide some focused prospects on the application areas in this field.

Keywords： 多智能体；博弈论；集体智能；强化学习；智能控制    

HTML PDF Collect

Decentralized multi-agent reinforcement learning with networked agents: recent advances Review Article

Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6,   Pages 802-814 doi: 10.1631/FITEE.1900661

Abstract: Multi-agent (MARL) has long been a significant research topic in both machine learning and control systems. Recent development of (single-agent) deep has created a resurgence of interest in developing new MARL algorithms, especially those founded on theoretical analysis. In this paper, we review recent advances on a sub-area of this topic: decentralized MARL with networked agents. In this scenario, multiple agents perform sequential decision-making in a common environment, and without the coordination of any central controller, while being allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and the smart grid. This review covers several of our research endeavors in this direction, as well as progress made by other researchers along the line. We hope that this review promotes additional research efforts in this exciting yet challenging area.

Keywords： 强化学习；多智能体系统；网络系统；一致性优化；分布式优化；博弈论    

HTML PDF Collect

Multi-agent differential game based cooperative synchronization control using a data-driven method Research Article

Yu SHI, Yongzhao HUA, Jianglong YU, Xiwang DONG, Zhang REN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1043-1056 doi: 10.1631/FITEE.2200001

Abstract: This paper studies the multi-agent based problem and its application to cooperative . A systematized formulation and analysis method for the multi-agent is proposed and a methodology based on the (RL) technique is given. First, it is pointed out that typical distributed controllers may not necessarily lead to global Nash equilibrium of the in general cases because of the coupling of networked interactions. Second, to this end, an alternative local Nash solution is derived by defining the best response concept, while the problem is decomposed into local s. An off-policy RL algorithm using neighboring interactive data is constructed to update the controller without requiring a system model, while the stability and robustness properties are proved. Third, to further tackle the dilemma, another configuration is investigated based on modified coupling index functions. The distributed solution can achieve global Nash equilibrium in contrast to the previous case while guaranteeing the stability. An equivalent parallel RL method is constructed corresponding to this Nash solution. Finally, the effectiveness of the learning process and the stability of are illustrated in simulation results.

Keywords： Multi-agent system     Differential game     Synchronization control     Data-driven     Reinforcement learning    

HTML PDF Collect

Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents Research Article

Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1032-1042 doi: 10.1631/FITEE.2100594

Abstract: Multi-agent is difficult to apply in practice, partially because of the gap between simulated and real-world scenarios. One reason for the gap is that simulated systems always assume that agents can work normally all the time, while in practice, one or more agents may unexpectedly "crash" during the coordination process due to inevitable hardware or software failures. Such crashes destroy the cooperation among agents and lead to performance degradation. In this work, we present a formal conceptualization of a cooperative multi-agent system with unexpected crashes. To enhance the robustness of the system to crashes, we propose a coach-assisted multi-agent framework that introduces a virtual coach agent to adjust the crash rate during training. We have designed three coaching strategies (fixed crash rate, curriculum learning, and adaptive crash rate) and a re-sampling strategy for our coach agent. To our knowledge, this work is the first to study unexpected crashes in a . Extensive experiments on grid-world and StarCraft II micromanagement tasks demonstrate the efficacy of the adaptive strategy compared with the fixed crash rate strategy and curriculum learning strategy. The ablation study further illustrates the effectiveness of our re-sampling strategy.

Keywords： Multi-agent system     Reinforcement learning     Unexpected crashed agents    

HTML PDF Collect

Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game Research Article

Hongyang LI, Qinglai WEI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1010-1019 doi: 10.1631/FITEE.2200010

Abstract: This paper presents a novel method for with . The multi-agent game theory is introduced to transform the problem into a multi-agent . Then, the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman (HJB) equations with nonquadratic input energy terms. A novel method is presented to obtain the Nash equilibrium solution without the system models, and the critic neural networks (NNs) and actor NNs are introduced to implement the presented method. Theoretical analysis is provided, which shows that the iterative control laws converge to the Nash equilibrium. Simulation results show the good performance of the presented method.

Keywords： Optimal synchronization control     Multi-agent systems     Nonzero-sum game     Adaptive dynamic programming     Input saturation     Off-policy reinforcement learning     Policy iteration    

HTML PDF Collect

The Application of Multi-agent Based Distributed Intelligent Control in VAV Air Conditioning System

Zhang Hongwei,Wu Aiguo,Sheng Tao

Strategic Study of CAE 2006, Volume 8, Issue 7,   Pages 58-62

Abstract:

A VAV system can be treated as a multi-agent system. In this paper, a multi-agent-based distributed intelligent control method is presented to solve the problem of concordance and decoupling in the VAV system. A simulation program of VAV system is set up for control analysis. Through a simulation, this control method has been proved to be satisfactory.

Keywords： VAV     agent     multi-agent system     distributed intelligent control    

HTML PDF Collect

Existence and practice of gaming: thoughts on the development of multi-agent system gaming Perspective

Qi DONG, Zhenyu WU, Jun LU, Fengsong SUN, Jinyu WANG, Yanyu YANG, Xiaozhou SHANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 995-1001 doi: 10.1631/FITEE.2100593

Abstract: Game is a universal being in the universe. Starting with human understanding of the game process, we discuss the existence and practice of gaming, expound challenges in multi-agent gaming, and put forward a theoretical framework for a multiagent evolutionary game based on the idea of evolution and system theory. Taking the next-generation early warning and detection system as an example, we introduce the applications of multi-agent evolutionary game. We construct a multi-agent selforganizing game decision-making model and develop a multi-agent method based on reinforcement learning, which are significant in studying organized and systematic game behaviors in a high-dimensional complex environment.

Keywords： 博弈；多智能体系统；多智能体演化博弈；预警探测    

HTML PDF Collect

Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wireless networks Research Article

Xiaoyu LIU, Chi XU, Haibin YU, Peng ZENG,liuxiaoyu1@sia.cn,xuchi@sia.cn,yhb@sia.cn,zp@sia.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 1,   Pages 47-60 doi: 10.1631/FITEE.2100331

Abstract: Edge artificial intelligence will empower the ever simple (IWNs) supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices (MTDs) and edge servers. In this paper, we propose a based resource allocation (MADRL-RA) algorithm for IWNs to support computation-intensive and -sensitive applications. First, we present the system model of IWNs, wherein each MTD is regarded as a self-learning agent. Then, we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of and . Next, we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision, computation capacity, and transmission power. To break the time correlation of training data while accelerating the learning process of MADRL-RA, we design a weighted experience replay to store and sample experiences categorically. Furthermore, we propose a step-by-step -greedy method to balance exploitation and exploration. Finally, we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments, showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.

Keywords： Multi-agent deep reinforcement learning     End–edge orchestrated     Industrial wireless networks     Delay     Energy consumption    

HTML PDF Collect

Cooperative channel assignment for VANETs based on multiagent reinforcement learning Research Articles

Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 1047-1058 doi: 10.1631/FITEE.1900308

Abstract: (DCA) plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion. However, channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes, the lack of centralized coordination, unknown global state information, and other challenges. To solve this problem, a multiagent (RL) based cooperative DCA (RL-CDCA) mechanism is proposed. Specifically, each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information (CSI) using two cooperative RL models. In addition, neural networks are constructed as nonlinear Q-function approximators, which facilitates the mapping of the continuously sensed input to the mixed policy output. Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner. Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%, improve the packet delivery ratio by no less than 12.66% on average in a highly dense situation, and improve the fairness of the global network resource allocation.

Keywords： Vehicular ad-hoc networks     Reinforcement learning     Dynamic channel assignment     Multichannel    

HTML PDF Collect

Artificial Intelligence for Retrosynthesis Prediction Review

Yinjie Jiang, Yemin Yu, Ming Kong, Yu Mei, Luotian Yuan, Zhengxing Huang, Kun Kuang, Zhihua Wang, Huaxiu Yao, James Zou, Connor W. Coley, Ying Wei

Engineering 2023, Volume 25, Issue 6,   Pages 32-50 doi: 10.1016/j.eng.2022.04.021

Abstract:

In recent years, there has been a dramatic rise in interest in retrosynthesis prediction with artificial intelligence (AI) techniques. Unlike conventional retrosynthesis prediction performed by chemists and by rule-based expert systems, AI-driven retrosynthesis prediction automatically learns chemistry knowledge from off-the-shelf experimental datasets to predict reactions and retrosynthesis routes. This provides an opportunity to address many conventional challenges, including heavy reliance on extensive expertise, the sub-optimality of routes, and prohibitive computational cost. This review describes the current landscape of AI-driven retrosynthesis prediction. We first discuss formal definitions of the retrosynthesis problem and review the outstanding research challenges therein. We then review the related AI techniques and recent progress that enable retrosynthesis prediction. Moreover, we propose a novel landscape that provides a comprehensive categorization of different retrosynthesis prediction components and survey how AI reshapes each component. We conclude by discussing promising areas for future research.

Keywords： Retrosynthesis prediction     Artificial intelligence     Graph neural networks     Deep reinforcement learning    

HTML PDF Collect

Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1332-1348 doi: 10.1631/FITEE.2200299

Abstract: Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation. However, existing methods often fall into what we call interactive misunderstanding, the essence of which is the dilemma in trading off short- and long-term interaction information. To better use the interaction information at various timescales, we propose an interactive segmentation framework, called interactive MEdical image segmentation with self-adaptive Confidence CAlibration (MECCA), which combines action-based confidence learning and multi-agent reinforcement learning. A novel confidence network is learned by predicting the alignment level of the action with short-term interaction information. A confidence-based reward-shaping mechanism is then proposed to explicitly incorporate confidence in the policy gradient calculation, thus directly correcting the model’s interactive misunderstanding. MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance, respectively. Numerical experiments on different segmentation tasks show that MECCA can significantly improve short- and long-term interaction information utilization efficiency with remarkably fewer labeled samples. The demo video is available at https://bit.ly/mecca-demo-video.

Keywords： Medical image segmentation     Interactive segmentation     Multi-agent reinforcement learning     Confidence learning     Semi-supervised learning    

HTML Collect

Intelligent Products and Equipment Led by New-Generation Artificial Intelligence

Tan Jianrong, Liu Zhenyu, Xu Jinghua

Strategic Study of CAE 2018, Volume 20, Issue 4,   Pages 35-43 doi: 10.15302/J-SSCAE-2018.04.007

Abstract:

Intelligent products and equipment is the value carrier, technological prerequisite and material base of intelligent manufacturing and service. The intelligent products and equipment refers to two dialectical aspects: on the one hand, commercialization of intelligent technology, turning intelligence technology into products, which is mainly reflected in the comprehensive application of the Internet of Things, big data, cloud computing, edge computing, machine learning, deep learning, security monitoring, automation control, computer technology, precision sensing technology, and GPS positioning technology; On the other hand, the intelligent products and equipment refers to the intellectualization of traditional products. The new-generation artificial intelligence endows traditional products with higher intelligence and injects strong vitality and developmental motivation into traditional products in the aspect of intelligent manufacturing equipment, intelligent production, and intelligent management. Based on extensive scientific surveys and current researches, and combined with the ten major fields of Made in China 2025 and macro policies such as the Three-Year Action Plan for Artificial Intelligence, twelve major equipment fields of intelligent products and equipment are formulated. Researches show that the new-generation intelligent products and equipment focuses on knowledge engineering and is prominently characterized by self-sensing, self-adaptation, self-learning, and self-decision-making. Ten key technologies will be prioritized in future.

Keywords： intelligent products and equipment     knowledge engineering     intelligent state sensing     intelligent variation adaptation     intelligent knowledge learning     intelligent control decision    

HTML PDF Collect

Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous Driving Article

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Engineering 2023, Volume 21, Issue 2,   Pages 75-91 doi: 10.1016/j.eng.2022.05.017

Abstract:

Due to its limited intelligence and abilities, machine learning is currently unable to handle various situations thus cannot completely replace humans in real-world applications. Because humans exhibit robustness and adaptability in complex scenarios, it is crucial to introduce humans into the training loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learning algorithms. In this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developed for policy training in an end-to-end autonomous driving case. With our newly designed mechanism for control transfer between humans and automation, humans are able to intervene and correct the agent's unreasonable actions in real time when necessary during the model training process. Based on this human-in-the-loop guidance mechanism, an improved actor-critic architecture with modified policy and value networks is developed. The fast convergence of the proposed Hug-DRL allows real-time human guidance actions to be fused into the agent's training loop, further improving the efficiency and performance of DRL. The developed method is validated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning approaches. The results suggest that the proposed method can effectively enhance the training efficiency and performance of the DRL algorithm under human guidance without imposing specific requirements on participants' expertise or experience.

Keywords： Human-in-the-loop AI     Deep reinforcement learning     Human guidance     Autonomous driving    

HTML PDF Collect

Resource allocation for network profit maximization in NOMA-based F-RANs: a game-theoretic approach Research Article

Xueyan CAO, Shi YAN, Hongming ZHANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1546-1561 doi: 10.1631/FITEE.2100341

Abstract:

(NOMA) based s (F-RANs) offer high spectrum efficiency, ultra-low delay, and huge network throughput, and this is made possible by edge computing and communication functions of the fog access points (F-APs). Meanwhile, caching-enabled F-APs are responsible for edge caching and delivery of a large volume of multimedia files during the caching phase, which facilitates further reduction in the transmission energy and burden. The need of the prevailing situation in industry is that in NOMA-based F-RANs, energy-efficient , which consists of (CP) and radio (RRA), is crucial for network performance enhancement. To this end, in this paper, we first characterize an NOMA-based F-RAN in which F-APs of caching capabilities underlaid with the radio remote heads serve user equipments via the NOMA protocol. Then, we formulate a problem for maximizing the defined performance indicator, namely network profit, which takes caching cost, revenue, and energy efficiency into consideration. The NP-hard problem is decomposed into two sub-problems, namely the CP sub-problem and RRA sub-problem. Finally, we propose an iterative method and a Stackelberg game based method to solve them, and numerical results show that the proposed solution can significantly improve network profit compared to some existing schemes in NOMA-based F-RANs.

Keywords： Fog radio access network     Non-orthogonal multiple access     Game theory     Cache placement     Resource allocation    

HTML PDF Collect

Title Author Date Type Operation

Prospects for multi-agent collaboration and gaming: challenge, technology, and application

Yu LIU, Zhi LI, Zhizhuo JIANG, You HE

Journal Article

Decentralized multi-agent reinforcement learning with networked agents: recent advances

Kaiqing Zhang, Zhuoran Yang, Tamer Başar,kzhang66@illinois.edu,zy6@princeton.edu,basar1@illinois.edu

Journal Article

Multi-agent differential game based cooperative synchronization control using a data-driven method

Yu SHI, Yongzhao HUA, Jianglong YU, Xiwang DONG, Zhang REN

Journal Article

Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents

Jian ZHAO, Youpeng ZHAO, Weixun WANG, Mingyu YANG, Xunhan HU, Wengang ZHOU, Jianye HAO, Houqiang LI

Journal Article

Optimal synchronization control for multi-agent systems with input saturation: a nonzero-sum game

Hongyang LI, Qinglai WEI

Journal Article

The Application of Multi-agent Based Distributed Intelligent Control in VAV Air Conditioning System

Zhang Hongwei,Wu Aiguo,Sheng Tao

Journal Article

Existence and practice of gaming: thoughts on the development of multi-agent system gaming

Qi DONG, Zhenyu WU, Jun LU, Fengsong SUN, Jinyu WANG, Yanyu YANG, Xiaozhou SHANG

Journal Article

Multi-agent deep reinforcement learning for end–edge orchestrated resource allocation in industrial wireless networks

Xiaoyu LIU, Chi XU, Haibin YU, Peng ZENG,liuxiaoyu1@sia.cn,xuchi@sia.cn,yhb@sia.cn,zp@sia.cn

Journal Article

Cooperative channel assignment for VANETs based on multiagent reinforcement learning

Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Journal Article

Artificial Intelligence for Retrosynthesis Prediction

Yinjie Jiang, Yemin Yu, Ming Kong, Yu Mei, Luotian Yuan, Zhengxing Huang, Kun Kuang, Zhihua Wang, Huaxiu Yao, James Zou, Connor W. Coley, Ying Wei

Journal Article

Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Journal Article

Intelligent Products and Equipment Led by New-Generation Artificial Intelligence

Tan Jianrong, Liu Zhenyu, Xu Jinghua

Journal Article

Toward Human-in-the-loop AI: Enhancing Deep Reinforcement Learning Via Real-time Human Guidance for Autonomous Driving

Jingda Wu, Zhiyu Huang, Zhongxu Hu, Chen Lv

Journal Article

Resource allocation for network profit maximization in NOMA-based F-RANs: a game-theoretic approach

Xueyan CAO, Shi YAN, Hongming ZHANG

Journal Article

Intellectual control—A goal exceeding the century - speech on the Fourteenth Conference IFAC

Song Jian

Journal Article