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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Sensen Liang, Xun Niu, Simin Niu, Zhendong Wu, Bo Hua, Gengxin Wang, Long Guan, Zhenyu Wang, Hanyu Zhang, Xuan Li, Zhiyu Ma, Yuefeng School of Information Renmin University of China China Beijing Institute of Technology China Xiangjiang Laboratory Central South University China Beijing Institute of Control Engineering China Western Xia Research Institute Ningxia University China Harvest Fund Management Co. Ltd. China Shanghai Algorithm Innovation Research Institute China School of Computer Qufu Normal University China

ISBN: (纸本)157735897X

The significance of Temporal Knowledge Graphs (TKGs) in Artificial Intelligence (AI) lies in their capacity to incorporate time-dimensional information, support complex reasoning and prediction, optimize decision-making processes, enhance the accuracy of recommendation systems, promote multimodal data integration, and strengthen knowledge management and updates. This provides a robust foundation for various AI applications. To effectively learn and apply both static and dynamic temporal patterns for reasoning, a range of embedding methods and Large Language Models (LLMs) have been proposed in the literature. However, these methods often rely on a single underlying embedding space, whose geometric properties severely limit their ability to model intricate temporal patterns, such as hierarchical and ring structures. To address this limitation, this paper proposes embedding TKGs into projective geometric space and leverages LLMs technology to extract crucial temporal node information, thereby constructing the 5EL model. By embedding TKGs into projective geometric space and utilizing Möbius Group transformations, we effectively model various temporal patterns. Subsequently, LLMs technology is employed to process the trained TKGs. We adopt a parameter-efficient fine-tuning strategy to align LLMs with specific task requirements, thereby enhancing the model's ability to recognize structural information of key nodes in historical chains and enriching the representation of central entities. Experimental results on five advanced TKG datasets demonstrate that our proposed 5EL model significantly outperforms existing models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

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Risk-Sensitive Reinforcement Learning with Exponential Criteria

arXiv

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arXiv 2022年

作者： Noorani, Erfaun Mavridis, Christos N. Baras, John S. Department of Electrical and Computer Engineering the Institute for Systems Research University of Maryland College Park United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

While reinforcement learning has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variability in the total reward amongst different episodes on slightly different environments. To introduce robustness, as well as sample efficiency, risk-sensitive reinforcement learning methods are being thoroughly studied. In this work, we provide a definition of robust reinforcement learning policies and formulate a risk-sensitive reinforcement learning problem to approximate them, by solving an optimization problem with respect to a modified objective based on exponential criteria. In particular, we study a model-free risk-sensitive variation of the widely-used Monte Carlo Policy Gradient algorithm, and introduce a novel risk-sensitive online Actor-Critic algorithm based on solving a multiplicative Bellman equation using stochastic approximation updates. Analytical results suggest that the use of exponential criteria generalizes commonly used ad-hoc regularization approaches, improves sample efficiency, and introduces robustness with respect to perturbations in the model parameters and the environment. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments. © 2022, CC BY.

关键词： Contrastive Learning

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MPC control Strategy for Autonomous Vehicles Driving in Roundabouts

MPC Control Strategy for Autonomous Vehicles Driving in Roun...

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Mediterranean Conference on control and Automation (MED)

作者： Zsó fia Farkas Andrá s Mihá ly Pé ter Gá spá r Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd Research Network (ELKH) Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

The appearance of autonomous vehicles (AVs) in transportation has increased the attention of the scientific community to develop modern solutions for the control design of AVs in different traffic scenarios. In this paper a control method is proposed for the coordination of autonomous vehicles in roundabout scenarios. For collision avoidance and minimization of traveling time, a Model Predictive control (MPC) with a centralized controller is introduced to calculate the traveling times of the vehicles. A presented algorithm determines velocity profiles for safety reasons and for the reduction of possible congestion. The operation of the proposed MPC method is tested and demonstrated in CarSim simulation environment.

关键词： Automation control design Transportation Prediction algorithms Minimization Safety Collision avoidance

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Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions

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SICE Journal of control, Measurement, and System Integration 2021年第1期14卷 116-127页

作者： Omainska, Marco Yamauchi, Junya Beckers, Thomas Hatanaka, Takeshi Hirche, Sandra Fujita, Masayuki Department of Information Physics and Computing The University of Tokyo Tokyo Japan Department of Electrical and Systems Engineering University of Pennsylvania PennsylvaniaPA United States Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan Department of Electrical and Computer Engineering Technical University of Munich Munich Germany

In this paper, we propose an observer-based visual pursuit control integrating three-dimensional target motion learning by Gaussian Process Regression (GPR). We consider a situation where a visual sensor equipped rigid body pursuits a target rigid body whose velocity is unknown but dependent on the target's pose. We estimate the pose from visual information and propose a Gaussian Process (GP) model to predict the target velocity from the pose estimate. We analyse stability of the proposed control by showing that estimation and control errors are ultimately bounded with high probability. Finally, simulations illustrate the performance of the proposed control schemes even if the visual measurement is corrupted by noise. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Gaussian distribution

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Two-Player Incomplete Games of Resilient Multiagent Systems

arXiv

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arXiv 2022年

作者： Nugraha, Yurid E. Hayakawa, Tomohisa Ishii, Hideaki Cetinkaya, Ahmet Zhu, Quanyan Department of Electrical Engineering Sepuluh Nopember Institute of Technology Surabaya60111 Indonesia Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Department of Computer Science Tokyo Insitute of Technology Yokohama226-8502 Japan Department of Functional Control Systems Shibaura Institute of Technology Tokyo135-8548 Japan Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

Evolution of agents’ dynamics of multiagent systems under consensus protocol in the face of jamming attacks is discussed, where centralized parties are able to influence the control signals of the agents. In this paper we focus on a game-theoretical approach of multiagent systems where the players have incomplete information on their opponents’ strength. We consider repeated games with both simultaneous and sequential player actions where players update their beliefs of each other over time. The effect of the players’ optimal strategies according to Bayesian Nash Equilibrium and Perfect Bayesian Equilibrium on agents’ consensus is examined. It is shown that an attacker with incomplete knowledge may fail to prevent consensus despite having sufficient resources to do so. © 2022, CC BY-NC-ND.

关键词： Multi agent systems

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Ranking of attributes—comparative study based on data from stylometric domain

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Procedia computer Science 2022年 207卷 2737-2746页

作者： Beata Zielosko Urszula Stańczyk Krzysztof Żabiński Institute of Computer Science University of Silesia in Katowice Będzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors.

关键词： Feature selection Ranking of attributes Relief Standard deviation Decision rules Stylometry Supervised discretisation Authorship attribution

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Mechanism of overscreening breakdown by electrode surface morphology in asymmetric ionic liquids

arXiv

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arXiv 2024年

作者： Nesterova, Irina Khlyupin, Aleksey Evstigneev, Nikolay Ryabkov, Oleg Gerke, Kirill M. Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny Moscow141700 Russia Federal Research Center "Computer Science and Control" Institute for System Analysis Russian Academy of Science Vavilova str. 40 Moscow119333 Russia Schmidt Institute of Physics The Earth of Russian Academy of Sciences Bolshaya Gruzinskaya 10 Moscow123242 Russia

The interfacial nature of the electric double layer (EDL) assumes that electrode surface morphology significantly impacts the EDL properties. Since molecular-scale roughness modifies the structure of EDL, it is expected to disturb the overscreening effect and alter differential capacitance (DC). In this Letter, we present a model that describes EDL near atomically rough electrodes with account for short-range electrostatic correlations. We provide numerical and analytical solutions for the analysis of conditions for the overscreening breakdown and DC shift estimation. Our findings reveal that electrode surface structure leads to DC decrease and can both brake or enhance overscreening depending on the relation of surface roughness to electrostatic correlation length and ion size asymmetry. Copyright © 2024, The Authors. All rights reserved.

关键词： Surface morphology

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Shared Situational Awareness with V2X Communication and Set-membership Estimation

Shared Situational Awareness with V2X Communication and Set-...

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International Conference on Intelligent Transportation

作者： Vandana Narri Amr Alanwar Jonas Mårtensson Christoffer Norén Karl Henrik Johansson Research and Development Scania CV AB Södertälje Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Computation Information and Technology Technical University of Munich Heilbronn Germany School of Computer Science and Engineering Constructor University Bremen Germany

The ability to perceive and comprehend a traffic situation and to estimate the state of the vehicles and road-users in the surrounding of the ego-vehicle is known as situational awareness. Situational awareness for a heavy-duty autonomous vehicle is a critical part of the automation platform and depends on the ego-vehicle's field-of-view. But when it comes to the urban scenarios, the field-of-view of the ego-vehicle is likely to be affected by occlusions and blind spots caused by infrastructure, moving vehicles, and parked vehicles. This paper proposes a framework to improve situational awareness using set-membership estimation and Vehicle-to-Everything (V2X) communication. This framework provides safety guarantees and can adapt to dynamically changing scenarios, and is integrated into an existing complex autonomous platform. A detailed description of the framework implementation and realtime results are illustrated in this paper.

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Query-guided Prototype Evolution Network for Few-Shot Segmentation

arXiv

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arXiv 2024年

作者： Cong, Runmin Xiong, Hang Chen, Jinpeng Zhang, Wei Huang, Qingming Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support features for prototype generation, neglecting the specific requirements of the query. To address this, we present the Query-guided Prototype Evolution Network (QPENet), a new method that integrates query features into the generation process of foreground and background prototypes, thereby yielding customized prototypes attuned to specific queries. The evolution of the foreground prototype is accomplished through a support-query-support iterative process involving two new modules: Pseudo-prototype Generation (PPG) and Dual Prototype Evolution (DPE). The PPG module employs support features to create an initial prototype for the preliminary segmentation of the query image, resulting in a pseudo-prototype reflecting the unique needs of the current query. Subsequently, the DPE module performs reverse segmentation on support images using this pseudo-prototype, leading to the generation of evolved prototypes, which can be considered as custom solutions. As for the background prototype, the evolution begins with a global background prototype that represents the generalized features of all training images. We also design a Global Background Cleansing (GBC) module to eliminate potential adverse components mirroring the characteristics of the current foreground class. Experimental results on the PASCAL-5i and COCO-20i datasets attest to the substantial enhancements achieved by QPENet over prevailing state-of-the-art techniques, underscoring the validity of our ideas. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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Preface: Security and safety in artificial intelligence

引用

Security and Safety 2024年第4期3卷 4-5页

作者： Hao Zhang Yu-Gang Jiang Claudio Melchiorri Gerhard Rigoll Department of Control Science and Engineering Tongji UniversityShanghai 200092China Shanghai Institute of Intelligent Science and Technology Shanghai 201210China School of Computer Science Fudan UniversityShanghai 200433China Department of Electrical Electronic and Information EngineeringUniversity of BolognaBologna 40136Italy Institute for Human-Machine Communication Technical University of MunichMunich 80539Germany

This special topic centers on cutting-edge advancements in the security and safety of artificial intelligence(AI),with a focus on critical applications across domains such as autonomous systems,federated learning,and network *** rapid evolution of AI algorithms,enabled by advances in hardware and software,has led to transformative applications but also revealed significant vulnerabilities and security risks.

关键词： artificial hardware autonomous

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