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Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 37-57页

作者： Zhiqiang Pu Yi Pan Shijie Wang Boyin Liu Min Chen Hao Ma Yixiong Cui the School of artificial intelligence University of Chinese Academy of Sciences the institute of Automation Chinese Academy of Sciences the Nanjing artificial intelligence Research of IA the School of Sports Engineering Beijing Sport University

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

关键词： artificial intelligence(AI) decision-making football review soccer sports analytics

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Optics-driven drone

Science China(Information Sciences) 2024年第2期67卷 322-324页

作者： Xuelong LI Guan HUANG Zhigang WANG Bin ZHAO School of artificial intelligence OPtics and Electro Nics (iOPEN)Northwestern Polytechnical University Shanghai artificial intelligence Laboratory

In the field of vicinagearth security, low-altitude flying vehicles such as drones play an indispensable role in aerial remote sensing, intelligent search and rescue, etc. [1]. It can be foreseen that the autonomous and intelligent execution of complex tasks by drones is an inevitable trend for future development.

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A Bio-Inspired Integration Model of Basal Ganglia and Cerebellum for Motion Learning of a Musculoskeletal Robot

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Journal of Systems Science & Complexity 2024年第1期37卷 82-113页

作者： ZHANG Jinhan CHEN Jiahao ZHONG Shanlin QIAO Hong State Key Laboratory of Multimodal artificial intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China.

It is a significant research direction for highly complex musculoskeletal robots that how to develop the ability of motion learning and generalization.The cooperations of multiple brain regions are crucial to improving motion performance.Inspired by the neural mechanisms of structures,functions,and interconnections of basal ganglia and cerebellum,a biologically inspired integration model for motor learning of musculoskeletal robots is proposed.Based on the neural characteristics of the basal ganglia,the basal ganglia actor network,which mainly simulates the dorsal striatum,outputs motion commands,and the basal ganglia critic network,which simulates the ventral striatum,estimates actionstate values.Their network parameters are updated using the soft actor-critic method.Based on the sensorimotor prediction mechanism of the cerebellum,the cerebellum network evaluates the state feature extraction quality of the basal ganglia actor network and then updates the weights of its feature layer.This learning method is proven to converge to the optimal policy.Furthermore,drawing on the mechanism of dopaminergic dynamic regulation in the basal ganglia,the adaptive adjustment of target entropy and the dopaminergic experience replay are proposed to further improve the integration model,which contributes to the exploration-exploitation trade-off of motor learning.The bio-inspired integration model is validated on a musculoskeletal system.Experimental results indicate that this model can effectively control the musculoskeletal robot to accomplish the motion task from random starting locations to random target positions with high precision and robustness.

关键词： Basal ganglia and cerebellum bio-inspired integration model motion learning musculoskeletal robot reinforcement learning.

Non-Deterministic Liveness-Enforcing Supervisor Tolerant to Sensor-Reading Modification Attacks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 240-248页

作者： Dan You Shouguang Wang IEEE the School of Information and Electronic Engineering Sussex Artificial Intelligence InstituteZhejiang Gongshang UniversityHangzhou 310018China

In this paper, we study the supervisory control problem of discrete event systems assuming that cyber-attacks might occur. In particular, we focus on the problem of liveness enforcement and consider a sensor-reading modification attack(SM-attack) that may disguise the occurrence of an event as that of another event by intruding sensor communication channels. To solve the problem, we introduce non-deterministic supervisors in the paper, which associate to every observed sequence a set of possible control actions offline and choose a control action from the set randomly online to control the system. Specifically, given a bounded Petri net(PN) as the reference formalism and an SMattack, an algorithm that synthesizes a liveness-enforcing nondeterministic supervisor tolerant to the SM-attack is proposed for the first time.

关键词： Cyber-attacks cyber-physical system(CPS) liveness non-deterministic supervisors Petri net(PN)

Adaptive interaction driven by the learning effect in the spatial prisoner's dilemma

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Chinese Physics B 2024年第3期33卷 160-169页

作者：李佳奇张建磊刘群 institute of Intelligent Information Hexi UniversityGansu 734000China College of artificial intelligence Nankai UniversityTianjin 300350China

We propose a computing model in which individuals can automatically adjust their interaction intensity with their mentor according to the learning effect.This model is designed to investigate the cooperative dynamics of the spatial prisoner's dilemma.More specifically,when the cumulative payoff of a learner is more than his reference earning,he will strengthen his interaction with his mentor;otherwise,he will reduce it.The experimental results indicate that this mechanism can improve the emergence of cooperation in a networked population and that the driving coefficient of interaction intensity plays an important role in promoting cooperation.Interestingly,under a certain social dilemma condition,there exists a minimal driving coefficient that leads to optimal cooperation.This occurs due to a positive feedback effect between the individual's satisfaction frequency and the number of effective neighbors.Moreover,we find that the experimental results are in accord with theoretical predictions obtained from an extension of the classical pair-approximation method.Our conclusions obtained by considering relationships with mentors can provide a new perspective for future investigations into the dynamics of evolutionary games within structured populations.

关键词： self-adapting interaction evolutionary game mentor spatial prisoner's dilemma

Scene text recognition via dual character counting-aware visual and semantic modeling network

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Science China(Information Sciences) 2024年第3期67卷 313-314页

作者： Ke XIAO Anna ZHU Brian Kenji IWANA Cheng-Lin LIU School of Computer Science and artificial intelligence Wuhan University of Technology Human Interface Laboratory Kyushu University State Key Laboratory of Multimodal artificial intelligence Systems Institute of AutomationChinese Academy of Sciences School of artificial intelligence University of Chinese Academy of Sciences

Scene text recognition(STR) is drawing increasing attention nowadays due to its wide application in real life. Character counting information, as auxiliary information, has been shown to be effective in boosting text ... 详细信息

关键词： Decoding

A credibility-aware swarm-federated deep learning framework in internet of vehicles

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Digital Communications and Networks 2024年第1期10卷 150-157页

作者： Zhe Wang Xinhang Li Tianhao Wu Chen Xu Lin Zhang School of artificial intelligence Beijing University of Posts and TelecommunicationsBeijing100876China

Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

关键词： Swarm learning Federated deep learning Internet of vehicles Privacy Efficiency

Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation

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Computers, Materials & Continua 2024年第4期79卷 1319-1334页

作者： Chunbin Qin Xiaotian Ran School of artificial intelligence Henan UniversityZhengzhou450000China

Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.

关键词： Unsupervised image stitching deep homography estimation YOLOv8 attention mechanism

Equilibrium Strategy of the Pursuit-Evasion Game in Three-Dimensional Space

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 446-458页

作者： Nuo Chen Linjing Li Wenji Mao School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049 institute of Automation Chinese Academy of SciencesBeijing 100190China

The pursuit-evasion game models the strategic interaction among players, attracting attention in many realistic scenarios, such as missile guidance, unmanned aerial vehicles, and target defense. Existing studies mainly concentrate on the cooperative pursuit of multiple players in two-dimensional pursuit-evasion games. However, these approaches can hardly be applied to practical situations where players usually move in three-dimensional space with a three-degree-of-freedom control. In this paper,we make the first attempt to investigate the equilibrium strategy of the realistic pursuit-evasion game, in which the pursuer follows a three-degree-of-freedom control, and the evader moves freely. First, we describe the pursuer's three-degree-of-freedom control and the evader's relative coordinate. We then rigorously derive the equilibrium strategy by solving the retrogressive path equation according to the Hamilton-Jacobi-Bellman-Isaacs(HJBI) method, which divides the pursuit-evasion process into the navigation and acceleration phases. Besides, we analyze the maximum allowable speed for the pursuer to capture the evader successfully and provide the strategy with which the evader can escape when the pursuer's speed exceeds the threshold. We further conduct comparison tests with various unilateral deviations to verify that the proposed strategy forms a Nash equilibrium.

关键词： Differential game equilibrium strategy pursuit-evasion game threedegree-of-freedom control