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GDT: Multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space GDT：基于自适应分组动态拓扑空间的多代理强化学习框架

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-15 DOI: 10.1016/j.ins.2024.121646

Licheng Sun , Hongbin Ma , Zhentao Guo

{"title":"GDT: Multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space","authors":"Licheng Sun , Hongbin Ma , Zhentao Guo","doi":"10.1016/j.ins.2024.121646","DOIUrl":"10.1016/j.ins.2024.121646","url":null,"abstract":"<div><div>In many real-world scenarios, tasks involve coordinating multiple agents, such as managing robot clusters, drone swarms, and autonomous vehicles. These tasks are commonly addressed using Multi-Agent Reinforcement Learning (MARL). However, existing MARL algorithms often lack foresight regarding the number and types of agents involved, requiring agents to generalize across various task configurations. This may lead to suboptimal performance due to underestimated action values and the selection of less effective joint policies. To address these challenges, we propose a novel multi-agent deep reinforcement learning framework, called multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space (GDT). GDT utilizes a group mesh topology to interconnect the local action value functions of each agent, enabling effective coordination and knowledge sharing among agents. By computing three different interpretations of action value functions, GDT overcomes monotonicity constraints and derives more effective overall action value functions. Additionally, GDT groups agents with high similarity to facilitate parameter sharing, thereby enhancing knowledge transfer and generalization across different scenarios. Furthermore, GDT introduces a strategy regularization method for optimal exploration of multiple action spaces. This method assigns each agent an independent entropy temperature during exploration, enabling agents to efficiently explore potential actions and approximate total state values. Experimental results demonstrate that our approach, termed GDT, significantly outperforms state-of-the-art algorithms on Google Research Football (GRF) and the StarCraft Multi-Agent Challenge (SMAC). Particularly in SMAC tasks, GDT achieves a success rate of nearly 100% across almost all Hard Map and Super Hard Map scenarios. Additionally, we validate the effectiveness of our algorithm on Non-monotonic Matrix Games.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121646"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finite-time secure synchronization for stochastic complex networks with delayed coupling under deception attacks: A two-step switching control scheme 欺骗攻击下具有延迟耦合的随机复杂网络的有限时间安全同步：两步切换控制方案

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-15 DOI: 10.1016/j.ins.2024.121647

Jie Mi , Huaiqin Wu , Jinde Cao

{"title":"Finite-time secure synchronization for stochastic complex networks with delayed coupling under deception attacks: A two-step switching control scheme","authors":"Jie Mi , Huaiqin Wu , Jinde Cao","doi":"10.1016/j.ins.2024.121647","DOIUrl":"10.1016/j.ins.2024.121647","url":null,"abstract":"<div><div>This article is concerned with the finite-time secure synchronization (FNTS) in mean square for stochastic complex networks (SCNs) with time-varying delayed coupling under deception attacks, where attack is described by a Bernoulli's stochastic variable, and is performed in the communication channel between the controller and the actuator. With the help of an auxiliary function, a new Halanay inequality is developed for continuous differential stochastic functions. By utilizing the Lyapunov functional gradient inequality with variable coefficients, a criterion about the finite-time stability in mean square is established for nonlinear stochastic systems under the designed two-step attenuation scheme. In order to reduce controller update consumption and communication waste, a two-step switching control mechanism consisting of an event-triggered control (ETC) and a time-varying gain state feedback control, is devised to achieve the FNTS objective. By Lyapunov stability theory, inequality analysis technique and the proposed finite-time stability criterion, the finite-time synchronization conditions are addressed in terms of linear matrix inequality (LMIs), and the bound of stochastic settling time (SST) is estimated explicitly. Finally, a practical application example is given to illustrate the effectiveness of the proposed control scheme, and to verify the correctness of the analytical results.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121647"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Community structure testing by counting frequent common neighbor sets 通过计算频繁共邻集进行群落结构测试

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-15 DOI: 10.1016/j.ins.2024.121649

Zengyou He , Xiaolei Li , Lianyu Hu , Mudi Jiang , Yan Liu

{"title":"Community structure testing by counting frequent common neighbor sets","authors":"Zengyou He , Xiaolei Li , Lianyu Hu , Mudi Jiang , Yan Liu","doi":"10.1016/j.ins.2024.121649","DOIUrl":"10.1016/j.ins.2024.121649","url":null,"abstract":"<div><div>The detection of communities from a graph is a key issue in network science and graph data mining. However, existing community detection algorithms can always partition a given network/graph into different communities/subgraphs, even when no community structure exists. Obviously, it will lead to fruitless efforts and erroneous conclusions if we conduct the community detection procedure on a network without a community structure. Hence, prior to community detection, it is a must to test whether the community structure is present in the target network. Unfortunately, the community structure testing issue is still not revolved and existing solutions have some limitations. Therefore, we present a new test, which is called FCN (Frequent Common Neighbor) test to tackle the community structure testing problem. In FCN test, the number of FCN sets is employed as the test statistic, which will approximately follows a Poisson distribution when the support threshold is sufficiently large under the null hypothesis that the graph is generated according to the Erdős-Rényi model. We compare the proposed FCN test with existing community structure testing methods on both real networks and simulated networks. The experimental results demonstrate the effectiveness and advantage of our method.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121649"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive granular data compression and interval granulation for efficient classification 自适应粒度数据压缩和区间粒度化，实现高效分类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-15 DOI: 10.1016/j.ins.2024.121644

Kecan Cai , Hongyun Zhang , Miao Li , Duoqian Miao

{"title":"Adaptive granular data compression and interval granulation for efficient classification","authors":"Kecan Cai , Hongyun Zhang , Miao Li , Duoqian Miao","doi":"10.1016/j.ins.2024.121644","DOIUrl":"10.1016/j.ins.2024.121644","url":null,"abstract":"<div><div>Efficiency is crucial in deep learning tasks and has garnered significant attention in green deep learning research field. However, existing methods often sacrifice efficiency for slight accuracy improvement, requiring extensive computational resources. This paper proposes an adaptive granular data compression and interval granulation method to improve classification efficiency without compromising accuracy. The approach comprises two main components: Adaptive Granular Data Compression (AG), and Interval Granulation (IG). Specifically, AG employs principle of justifiable granularity for adaptive generating granular data. AG enables the extraction of abstract granular subset representations from the original dataset, capturing essential features and thereby reducing computational complexity. The quality of the generated granular data is evaluated using coverage and specificity criteria, which are standard metrics in evaluating information granules. Furthermore, the design of IG performs AG operation on the input data at regular intervals during the training process. The multiple regular granulation operations during the training process increase the diversity of samples and help the model achieve better training. It is noteworthy that the proposed method can be extended to any convolution-based and attention-based classification neural network. Extensive experiments conducted on benchmark datasets demonstrate that the proposed method significantly enhances the classification efficiency without compromising accuracy.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121644"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safe reinforcement learning-based control using deep deterministic policy gradient algorithm and slime mould algorithm with experimental tower crane system validation 通过塔式起重机系统的实验验证，使用深度确定性策略梯度算法和粘模算法实现基于强化学习的安全控制

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-15 DOI: 10.1016/j.ins.2024.121640

Iuliu Alexandru Zamfirache , Radu-Emil Precup , Emil M. Petriu

{"title":"Safe reinforcement learning-based control using deep deterministic policy gradient algorithm and slime mould algorithm with experimental tower crane system validation","authors":"Iuliu Alexandru Zamfirache , Radu-Emil Precup , Emil M. Petriu","doi":"10.1016/j.ins.2024.121640","DOIUrl":"10.1016/j.ins.2024.121640","url":null,"abstract":"<div><div>This paper presents a novel optimal control approach resulting from the combination between the safe Reinforcement Learning (RL) framework represented by a Deep Deterministic Policy Gradient (DDPG) algorithm and a Slime Mould Algorithm (SMA) as a representative nature-inspired optimization algorithm. The main drawbacks of the traditional DDPG-based safe RL optimal control approach are the possible instability of the control system caused by randomly generated initial values of the controller parameters and the lack of state safety guarantees in the first iterations of the learning process due to (i) and (ii): (i) the safety constraints are considered only in the DDPG-based training process of the controller, which is usually implemented as a neural network (NN); (ii) the initial values of the weights and the biases of the NN-based controller are initialized with randomly generated values. The proposed approach mitigates these drawbacks by initializing the parameters of the NN-based controller using SMA. The fitness function of the SMA-based initialization process is designed to incorporate state safety constraints into the search process, resulting in an initial NN-based controller with embedded state safety constraints. The proposed approach is compared to the classical one using real-time experimental results and performance indices popular for optimal reference tracking control problems and based on a state safety score.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"692 ","pages":"Article 121640"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical feature likelihood evidential reasoning rule for equipment health state assessment considering asynchronous unequal interval data 考虑异步不等间隔数据的设备健康状态评估统计特征似然证据推理规则

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-15 DOI: 10.1016/j.ins.2024.121650

Chaoli Zhang , Zhijie Zhou , Jiayu Luo , Jie Wang

{"title":"Statistical feature likelihood evidential reasoning rule for equipment health state assessment considering asynchronous unequal interval data","authors":"Chaoli Zhang , Zhijie Zhou , Jiayu Luo , Jie Wang","doi":"10.1016/j.ins.2024.121650","DOIUrl":"10.1016/j.ins.2024.121650","url":null,"abstract":"<div><div>There are high-dimensional correlation variables and uncertainty asynchronous unequal interval data during the equipment test process, making it difficult to establish a health assessment model directly. Therefore, a statistical feature likelihood evidential reasoning rule for equipment health assessment is proposed, which achieves the alignment of the asynchronous unequal interval data, the decorrelation of the high-dimensional correlation variables, and the reduction of the assessment results uncertainty. Specifically, the reconstruction-based data stage division method is developed to determine the feature transformation reference value. Then, the independent evidence is constructed from the principal component features based on likelihood function normalization. Finally, the evidence activated by the feature samples is fused based on the evidential reasoning rule to assess the health state of the equipment. A numerical simulation case is conducted to demonstrate the implementation procedure. The advantage of the proposed method is verified by the case studies of the aircraft engine and the inertial measurement unit.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"692 ","pages":"Article 121650"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-preserving and communication-efficient stochastic alternating direction method of multipliers for federated learning 用于联合学习的隐私保护和通信效率高的随机交替方向乘法

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-14 DOI: 10.1016/j.ins.2024.121641

Yi Zhang , Yunfan Lu , Fengxia Liu , Cheng Li , Zixian Gong , Zhe Hu , Qun Xu

{"title":"Privacy-preserving and communication-efficient stochastic alternating direction method of multipliers for federated learning","authors":"Yi Zhang , Yunfan Lu , Fengxia Liu , Cheng Li , Zixian Gong , Zhe Hu , Qun Xu","doi":"10.1016/j.ins.2024.121641","DOIUrl":"10.1016/j.ins.2024.121641","url":null,"abstract":"<div><div>Federated learning constitutes a paradigm in distributed machine learning, wherein model training unfolds through the exchange of intermediary results between a central server and federated clients. Given its decentralized nature, conventional machine learning algorithms find limited applicability in the context of federated learning models. Hence, the alternating direction method of multipliers (ADMM), tailored for distributed optimization, is leveraged for this purpose. However, despite the considerable promise of the ADMM algorithm in federated learning, it faces challenges related to computational efficiency, communication efficiency, and data security. In response to these challenges, this study proposes the privacy-preserving and communication-efficient stochastic ADMM (PPCESADMM) algorithm that enhances the computational efficiency through the stochastic optimization method, reduces communication costs through sparse communication method, and ensures the security of federated clients' data via the homomorphic encryption method. Theoretical analyses confirm the convergence of the PPCESADMM algorithm under mild conditions and establish its convergence rate as <span><math><mi>O</mi><mo>(</mo><mn>1</mn><mo>/</mo><msqrt><mrow><mi>T</mi></mrow></msqrt><mo>)</mo></math></span>. Experiments illustrate the superior performance of our algorithm in communication cost compared to ADMM and CEADMM algorithms, achieving reductions of 65.10% and 44.32%, respectively. Furthermore, our method surpasses classical federated learning algorithms such as FedAvg, FedAvgM, and SCAFFOLD in terms of algorithmic convergence, achieving superior convergence precision within predefined training epochs. Finally, our algorithm converges to the same results as those obtained without using homomorphic encryption, albeit at the cost of increased computation time.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121641"},"PeriodicalIF":8.1,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Aggregation or separation? Adaptive embedding message passing for knowledge graph completion 聚合还是分离？知识图谱完成的自适应嵌入信息传递

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-13 DOI: 10.1016/j.ins.2024.121639

Zhifei Li , Lifan Chen , Yue Jian , Han Wang , Yue Zhao , Miao Zhang , Kui Xiao , Yan Zhang , Honglian Deng , Xiaoju Hou

{"title":"Aggregation or separation? Adaptive embedding message passing for knowledge graph completion","authors":"Zhifei Li , Lifan Chen , Yue Jian , Han Wang , Yue Zhao , Miao Zhang , Kui Xiao , Yan Zhang , Honglian Deng , Xiaoju Hou","doi":"10.1016/j.ins.2024.121639","DOIUrl":"10.1016/j.ins.2024.121639","url":null,"abstract":"<div><div>Knowledge graph completion intends to infer information within knowledge graphs, thereby bolstering the functionality of knowledge-driven applications. Recently, there has been a significant increase in the utilization of graph convolutional networks (GCNs) for knowledge graph completion. These GCN-based models primarily focus on aggregating information from neighboring entities and relations. Nonetheless, a fundamental question arises: is it beneficial to consider all neighbor information, and should some neighbor features be separated? We tackle this issue and present an adaptive graph convolutional network (AdaGCN) for knowledge graph completion, which can adaptively aggregate or separate neighbor information for knowledge embedding learning. Specifically, AdaGCN utilizes the adaptive message-passing mechanism to determine the importance of each relation, allocating weights to neighbor entity embeddings. This adaptive approach facilitates the propagation of valuable information while effectively separating less relevant or unnecessary details. Experimental results demonstrate that AdaGCN can efficiently acquire the embeddings of various triplets within knowledge graphs, and it achieves competitive performance compared to SOTA models on six datasets for the tasks of knowledge graph completion.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121639"},"PeriodicalIF":8.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating hybrid deep learning and path allocation for real-time inbound passenger flow prediction and anomaly detection in urban rail transit 将混合深度学习与路径分配相结合，用于城市轨道交通的实时进站客流预测和异常检测

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-12 DOI: 10.1016/j.ins.2024.121621

Huiran Liu , Zheng Wang , Zhiming Fang

{"title":"Integrating hybrid deep learning and path allocation for real-time inbound passenger flow prediction and anomaly detection in urban rail transit","authors":"Huiran Liu , Zheng Wang , Zhiming Fang","doi":"10.1016/j.ins.2024.121621","DOIUrl":"10.1016/j.ins.2024.121621","url":null,"abstract":"<div><div>This paper study the problem of real-time prediction of inbound passenger flow and the detection and alerting of abnormal passenger flows in urban rail transit (URT) networks. We propose a fused framework that combines a hybrid deep learning model and an evaluation strategy. Specifically, the learning model incorporates Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and attention mechanisms to effectively capture spatial and temporal correlations in passenger flow data. The evaluation strategy utilizes a depth-first search algorithm to determine the optimal travel paths for each individual passenger. And based on the paths, we develop a real-time method for estimating the origin–destination (OD) matrix that utilizes both long-term and short-term historical destination trend vectors to reduce dimensions while improving predictive accuracy. Through extensive testing using data from the Shanghai rail transit system, we demonstrate that this fused framework achieves high prediction accuracy for inbound passenger flow at various stations while efficiently identifying and warning sudden large-scale events involving significant increases in passenger flow volume. This research contributes towards improving overall passenger experience as well as operational resilience within urban rail systems when dealing with large-scale influxes of passengers.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"692 ","pages":"Article 121621"},"PeriodicalIF":8.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic quantization of event-triggered adaptive sliding mode control for networked control systems under false data injection attack 虚假数据注入攻击下网络控制系统事件触发自适应滑模控制的动态量化

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2024-11-12 DOI: 10.1016/j.ins.2024.121626

Xinggui Zhao, Bo Meng, Zhen Wang

引用次数: 0