The increasing trend toward dematerialization and digitalization has prompted a surge in the adoption of IT service providers, offering cost-effective alternatives to traditional local services. Consequently, cloud se...The increasing trend toward dematerialization and digitalization has prompted a surge in the adoption of IT service providers, offering cost-effective alternatives to traditional local services. Consequently, cloud services have become prevalent across various industries. While these services offer undeniable benefits, they face significant threats, particularly concerning the sensitivity of the data they handle. Many existing mathematical models struggle to accurately depict the complex scenarios of cloud systems. In response to this challenge, this paper proposes a behavioral model for ransomware propagation within such environments. In this model, each component of the environment is defined as an agent responsible for monitoring the propagation of malware. Given the distinct characteristics and criticality of these agents, the impact of malware can vary significantly. Scenario attacks are constructed based on real-world vulnerabilities documented in the Common Vulnerabilities and Exposures (CVEs) through the National Vulnerability Database. Defender actions are guided by an Intrusion Detection System (IDS) guideline. This research aims to provide a comprehensive framework for understanding and addressing ransomware threats in cloud systems. By leveraging an agent- based approach and real-world vulnerability data, our model offers valuable insights into detection and mitigation strategies for safeguarding sensitive cloud-based assets.展开更多
In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environments, as well as the need to increase the performance and safety characteristics of the related coo...In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environments, as well as the need to increase the performance and safety characteristics of the related cooperation, coordi-nation and control mechanisms is encouraging the development of new information management strategies to direct and man- age the automated systems involved in the manufacturing processes. The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems. In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions. Autonomy, flexibility and adaptability of the agent-based technology are the key points to manage both automated and information processes of any industrial system. The paper describes the main features of the IAs and MASs and how their technology can be adapted to support the current and next generation advanced industrial systems. Moreover, a study of how a MAS is utilized within a productive process is depicted.展开更多
It is impossible to plan in advance unpredictable phenomena at monitoring, diagnosis and control of industrial batch and continuous equipment and processes such as chemical composition of the raw materials, the proces...It is impossible to plan in advance unpredictable phenomena at monitoring, diagnosis and control of industrial batch and continuous equipment and processes such as chemical composition of the raw materials, the process leads to unexpected reactions and changes its parameters, etc. The agent is active, a program entity, has its own ideas how to perform the tasks of the own agenda. Agents perceive, behave ＂reasonably＂, communicate with other agents. Agents can represent equipment and operations in batch processes as recommended by the ISA $88. Jadex system is based on Java language and on FIPA org. recommendations. The description of ripening tank T406 and recipe for yogurt production in the holding of MADETA Corp. in the Czech Rep. It is described modeling and displaying of＂normal＂ and error, fault unit state of the ripening tank. Agents are within the Jadex system and describing the behavior of ripening tank T406 with state diagrams-automata and assist in diagnosing of fault states. States are described in XML language-SCXML （State Charts XML）. Jadex Control Center-JCC represents a major access point to use for operating in real time.展开更多
We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties. It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite siz...We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties. It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite size around the swarm center in a finite time. Moreover, we extend our results to more general attraction/repulsion functions. Numerical simulations demonstrate that all agents will eventually enter into and remain in a bounded region around the swarm center which may exhibit complex spiral motion due to asymmetry of the coupling structure. The model in this paper is more general than isotropic swarms and our results provide further insight into the effect of the interaction pattern on individual motion in a swarm system.展开更多
In this paper, we consider a consensus tracking problem of a class of networked multi-agent systems(MASs)in non-affine pure-feedback form under a directed topology. A distributed adaptive tracking consensus control sc...In this paper, we consider a consensus tracking problem of a class of networked multi-agent systems(MASs)in non-affine pure-feedback form under a directed topology. A distributed adaptive tracking consensus control scheme is constructed recursively by the backstepping method, graph theory,neural networks(NNs) and the dynamic surface control(DSC)approach. The key advantage of the proposed control strategy is that, by the DSC technique, it avoids "explosion of complexity"problem along with the increase of the degree of individual agents and thus the computational burden of the scheme can be drastically reduced. Moreover, there is no requirement for prior knowledge about system parameters of individual agents and uncertain dynamics by employing NNs approximation technology.We then further show that, in theory, the designed control policy guarantees the consensus errors to be cooperatively semi-globally uniformly ultimately bounded(CSUUB). Finally, two examples are presented to validate the effectiveness of the proposed control strategy.展开更多
This paper examines a consensus problem in multiagent discrete-time systems, where each agent can exchange information only from its neighbor agents. A decentralized protocol is designed for each agent to steer all ag...This paper examines a consensus problem in multiagent discrete-time systems, where each agent can exchange information only from its neighbor agents. A decentralized protocol is designed for each agent to steer all agents to the same vector. The design condition is expressed in the form of a linear matrix inequality. Finally, a simulation example is presented and a comparison is made to demonstrate the effectiveness of the developed methodology.展开更多
Finite-time consensus problem of the leader-following multi-agent system under switching network topologies is studied in this paper. Based on the graph theory, matrix theory, homogeneity with dilation, and LaSalle＇s...Finite-time consensus problem of the leader-following multi-agent system under switching network topologies is studied in this paper. Based on the graph theory, matrix theory, homogeneity with dilation, and LaSalle＇s invariance principle, the control protocol of each agent using local information is designed, and the detailed analysis of the leader- following finite-time consensus is provided. Some examples and simulation results are given to illustrate the effectiveness of the obtained theoretical results.展开更多
In this paper, the leader-following tracking problem of fractional-order multi-agent systems is addressed. The dynamics of each agent may be heterogeneous and has unknown nonlinearities. By assumptions that the intera...In this paper, the leader-following tracking problem of fractional-order multi-agent systems is addressed. The dynamics of each agent may be heterogeneous and has unknown nonlinearities. By assumptions that the interaction topology is undirected and connected and the unknown nonlinear uncertain dynamics can be parameterized by a neural network, an adaptive learning law is proposed to deal with unknown nonlinear dynamics, based on which a kind of cooperative tracking protocols are constructed. The feedback gain matrix is obtained to solve an algebraic Riccati equation. To construct the fully distributed cooperative tracking protocols, the adaptive law is also adopted to adjust the coupling weight. With the developed control laws,we can prove that all signals in the closed-loop systems are guaranteed to be uniformly ultimately bounded. Finally, a simple simulation example is provided to illustrate the established result.展开更多
Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experi...Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.展开更多
For multi-agent systems based on the local information,the agents automatically converge to a common consensus state and the convergence speed is determined by the algebraic connectivity of the communication network.T...For multi-agent systems based on the local information,the agents automatically converge to a common consensus state and the convergence speed is determined by the algebraic connectivity of the communication network.To study fast consensus seeking problems of multi-agent systems in undirected networks,a consensus protocol is proposed which considers the average information of the agents＇ states in a certain time interval,and a consensus convergence criterion for the system is obtained.Based on the frequency-domain analysis and algebra graph theory,it is shown that if the time interval is chosen properly,then requiring the same maximum control effort the proposed protocol reaches consensus faster than the standard consensus protocol.Simulations are provided to demonstrate the effectiveness of these theoretical results.展开更多
The main goal of this paper is to design a team of agents that can accomplish multi-target pursuit formation using a developed leader-follower strategy. It is supposed that every target can accept a certain number of ...The main goal of this paper is to design a team of agents that can accomplish multi-target pursuit formation using a developed leader-follower strategy. It is supposed that every target can accept a certain number of agents. First, each agent can automatically choose its target based on the distance from the agent to the target and the number of agents accepted by the target. In view of the fact that all agents are randomly dispersed in the workplace at the initial time, we present a numbering strategy for them. During the movement of agents, not every agent can always obtain pertinent state information about the targets. So, a developed leader-follower strategy and a pursuit formation algorithm are proposed. Under the proposed method, agents with the same target can maintain a circle formation. Furthermore, it turns out that the pursuit formation algorithm for agents to the desired formation is convergent. Simulation studies are provided to illustrate the effectiveness of the proposed method.展开更多
The control problem of a class of parabolic distributed parameter systems (DPSs) is investigated by using mobile agents with capabilities of sensing and actuating. The guidance strategies of mobile agents based on cov...The control problem of a class of parabolic distributed parameter systems (DPSs) is investigated by using mobile agents with capabilities of sensing and actuating. The guidance strategies of mobile agents based on coverage optimization methods are proposed to improve the control performance of the system and make the state norm of the system converge to zero faster. The coverage optimization problems are constructed based on the measurement of each agent. By solving the coverage optimization problems, the local optimal moving direction of each agent can be obtained. Then the gradient-based agent motion control laws are established. With the indicator function and the surface delta function, this method is generalized to n-dimensional space, and suitable for any sensing region with piecewise smooth boundaries. The stability and control performance of the system are analyzed. Numerical simulations show the effectiveness of the proposed methods.展开更多
文摘The increasing trend toward dematerialization and digitalization has prompted a surge in the adoption of IT service providers, offering cost-effective alternatives to traditional local services. Consequently, cloud services have become prevalent across various industries. While these services offer undeniable benefits, they face significant threats, particularly concerning the sensitivity of the data they handle. Many existing mathematical models struggle to accurately depict the complex scenarios of cloud systems. In response to this challenge, this paper proposes a behavioral model for ransomware propagation within such environments. In this model, each component of the environment is defined as an agent responsible for monitoring the propagation of malware. Given the distinct characteristics and criticality of these agents, the impact of malware can vary significantly. Scenario attacks are constructed based on real-world vulnerabilities documented in the Common Vulnerabilities and Exposures (CVEs) through the National Vulnerability Database. Defender actions are guided by an Intrusion Detection System (IDS) guideline. This research aims to provide a comprehensive framework for understanding and addressing ransomware threats in cloud systems. By leveraging an agent- based approach and real-world vulnerability data, our model offers valuable insights into detection and mitigation strategies for safeguarding sensitive cloud-based assets.
文摘In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environments, as well as the need to increase the performance and safety characteristics of the related cooperation, coordi-nation and control mechanisms is encouraging the development of new information management strategies to direct and man- age the automated systems involved in the manufacturing processes. The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems. In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions. Autonomy, flexibility and adaptability of the agent-based technology are the key points to manage both automated and information processes of any industrial system. The paper describes the main features of the IAs and MASs and how their technology can be adapted to support the current and next generation advanced industrial systems. Moreover, a study of how a MAS is utilized within a productive process is depicted.
文摘It is impossible to plan in advance unpredictable phenomena at monitoring, diagnosis and control of industrial batch and continuous equipment and processes such as chemical composition of the raw materials, the process leads to unexpected reactions and changes its parameters, etc. The agent is active, a program entity, has its own ideas how to perform the tasks of the own agenda. Agents perceive, behave ＂reasonably＂, communicate with other agents. Agents can represent equipment and operations in batch processes as recommended by the ISA $88. Jadex system is based on Java language and on FIPA org. recommendations. The description of ripening tank T406 and recipe for yogurt production in the holding of MADETA Corp. in the Czech Rep. It is described modeling and displaying of＂normal＂ and error, fault unit state of the ripening tank. Agents are within the Jadex system and describing the behavior of ripening tank T406 with state diagrams-automata and assist in diagnosing of fault states. States are described in XML language-SCXML （State Charts XML）. Jadex Control Center-JCC represents a major access point to use for operating in real time.
基金Supported by National Nature Science Foundation of China （61074068, 60774009, 61034007）, and the Research Fund for the Doc- toral Program of Chinese Higher Education （200804220028）
基金This work was supported by the National Natural Science Foundation of China (No. 10372002,60274001) and the National Key Basic Research and Develop-ment Program (No.2002CB312200).
文摘We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties. It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite size around the swarm center in a finite time. Moreover, we extend our results to more general attraction/repulsion functions. Numerical simulations demonstrate that all agents will eventually enter into and remain in a bounded region around the swarm center which may exhibit complex spiral motion due to asymmetry of the coupling structure. The model in this paper is more general than isotropic swarms and our results provide further insight into the effect of the interaction pattern on individual motion in a swarm system.
基金Supported by National Basic Research Program of China （973 Program） （2010CB731800）, Key Project of Natural Science Fouudation of China （60934003）, National Natural Science Foundation of China （61074065, 60974018）, Natural Science Foundation of Hebei Province（F2012203119）, and the Science Foundation of Yanshan University for the Excellent Ph. D. Students （201204） The authors thank Chen Cai-Lian of the Shanghai Jiao Tong University for her comments on English polishing and problem formulation.
基金supported in part by the National Natural Science Foundation of Chin(61503194,61533010,61374055)the Ph.D.Programs Foundation of Ministry of Education of China(20110142110036)+6 种基金the Natural Science Foundation o Jiangsu Province(BK20131381,BK20140877)China Postdoctoral Scienc Foundation(2015M571788)Jiangsu Planned Projects for Postdoctoral Re search Funds(1402066B)the Foundation of the Key Laboratory of Marin Dynamic Simulation and Control for the Ministry of Transport(DMU)(DMU MSCKLT2016005)Jiangsu Government Scholarship for Overseas Studie(2017-037)the Key University Natural Science Research Project of Jiangsu Province(17KJA120003)the Scientific Foundation of Nanjing University of Posts and Telecommunications(NUPTSF)(NY214076)
文摘In this paper, we consider a consensus tracking problem of a class of networked multi-agent systems(MASs)in non-affine pure-feedback form under a directed topology. A distributed adaptive tracking consensus control scheme is constructed recursively by the backstepping method, graph theory,neural networks(NNs) and the dynamic surface control(DSC)approach. The key advantage of the proposed control strategy is that, by the DSC technique, it avoids "explosion of complexity"problem along with the increase of the degree of individual agents and thus the computational burden of the scheme can be drastically reduced. Moreover, there is no requirement for prior knowledge about system parameters of individual agents and uncertain dynamics by employing NNs approximation technology.We then further show that, in theory, the designed control policy guarantees the consensus errors to be cooperatively semi-globally uniformly ultimately bounded(CSUUB). Finally, two examples are presented to validate the effectiveness of the proposed control strategy.
基金supported by Deanship of Scientific research(CDSR)at KFUPM(RG-1316-1)
文摘This paper examines a consensus problem in multiagent discrete-time systems, where each agent can exchange information only from its neighbor agents. A decentralized protocol is designed for each agent to steer all agents to the same vector. The design condition is expressed in the form of a linear matrix inequality. Finally, a simulation example is presented and a comparison is made to demonstrate the effectiveness of the developed methodology.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.60834002,60873021,and 61004042)the Youth Science Research Project of Chongqing University of Posts and Telecommunications,China(Grant No.A2012-82)the Doctor Start-up Foundation of Chongqing University of Posts and Telecommunications,China(Grant No.A2012-23)
文摘Finite-time consensus problem of the leader-following multi-agent system under switching network topologies is studied in this paper. Based on the graph theory, matrix theory, homogeneity with dilation, and LaSalle＇s invariance principle, the control protocol of each agent using local information is designed, and the detailed analysis of the leader- following finite-time consensus is provided. Some examples and simulation results are given to illustrate the effectiveness of the obtained theoretical results.
基金supported by the National Natural Science Foundation of China(61303211)Zhejiang Provincial Natural Science Foundation of China(LY17F030003,LY15F030009)
文摘In this paper, the leader-following tracking problem of fractional-order multi-agent systems is addressed. The dynamics of each agent may be heterogeneous and has unknown nonlinearities. By assumptions that the interaction topology is undirected and connected and the unknown nonlinear uncertain dynamics can be parameterized by a neural network, an adaptive learning law is proposed to deal with unknown nonlinear dynamics, based on which a kind of cooperative tracking protocols are constructed. The feedback gain matrix is obtained to solve an algebraic Riccati equation. To construct the fully distributed cooperative tracking protocols, the adaptive law is also adopted to adjust the coupling weight. With the developed control laws,we can prove that all signals in the closed-loop systems are guaranteed to be uniformly ultimately bounded. Finally, a simple simulation example is provided to illustrate the established result.
基金National Natural Science Foundation of China,Grant/Award Number:61872171The Belt and Road Special Foundation of the State Key Laboratory of Hydrology‐Water Resources and Hydraulic Engineering,Grant/Award Number:2021490811。
文摘Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents.However,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent systems.To tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency.These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state.In addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning.MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels.The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.
基金Supported by National Basic Research Program of China （973 Program） （2010CB731800）, National Natural Science Foundation of China （60934003, 61074065）, Key Project for Natural Science Research of Hebei Education Department （ZD200908）, and the Doctor Foundation of Northeastern University at Qinhuangdao（XNB201507）
基金supported by the National Natural Science Foundation of China (6087405360574088)
文摘For multi-agent systems based on the local information,the agents automatically converge to a common consensus state and the convergence speed is determined by the algebraic connectivity of the communication network.To study fast consensus seeking problems of multi-agent systems in undirected networks,a consensus protocol is proposed which considers the average information of the agents＇ states in a certain time interval,and a consensus convergence criterion for the system is obtained.Based on the frequency-domain analysis and algebra graph theory,it is shown that if the time interval is chosen properly,then requiring the same maximum control effort the proposed protocol reaches consensus faster than the standard consensus protocol.Simulations are provided to demonstrate the effectiveness of these theoretical results.
基金Project partially supported by the National Basic Research Program of China(Grant No.2010CB731800)the Key Project of Natural Science Foundation of China(Grant No.60934003)+1 种基金the National Natural Science Foundation of China(Grant No.61074065)Key Project for Natural Science Research of Hebei Education Department,China(Grant No.ZD200908)
文摘The main goal of this paper is to design a team of agents that can accomplish multi-target pursuit formation using a developed leader-follower strategy. It is supposed that every target can accept a certain number of agents. First, each agent can automatically choose its target based on the distance from the agent to the target and the number of agents accepted by the target. In view of the fact that all agents are randomly dispersed in the workplace at the initial time, we present a numbering strategy for them. During the movement of agents, not every agent can always obtain pertinent state information about the targets. So, a developed leader-follower strategy and a pursuit formation algorithm are proposed. Under the proposed method, agents with the same target can maintain a circle formation. Furthermore, it turns out that the pursuit formation algorithm for agents to the desired formation is convergent. Simulation studies are provided to illustrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(61807016 61174021)+3 种基金the Fundamental Research Funds for the Central Universities(JUSRP115A28 JUSRP51733B)the 111 Projeet(B12018)the Postgraduate Innovation Project of Jiangsu Province(KYLX151170)
文摘The control problem of a class of parabolic distributed parameter systems (DPSs) is investigated by using mobile agents with capabilities of sensing and actuating. The guidance strategies of mobile agents based on coverage optimization methods are proposed to improve the control performance of the system and make the state norm of the system converge to zero faster. The coverage optimization problems are constructed based on the measurement of each agent. By solving the coverage optimization problems, the local optimal moving direction of each agent can be obtained. Then the gradient-based agent motion control laws are established. With the indicator function and the surface delta function, this method is generalized to n-dimensional space, and suitable for any sensing region with piecewise smooth boundaries. The stability and control performance of the system are analyzed. Numerical simulations show the effectiveness of the proposed methods.
