With the improvement of mobile equipment performance and development of Pervasive Computing,interactive computational applications such as Multi-Agent (MA) systems in Pervasive Computing Environments (PCE) become more...With the improvement of mobile equipment performance and development of Pervasive Computing,interactive computational applications such as Multi-Agent (MA) systems in Pervasive Computing Environments (PCE) become more and more prevalent. Many applications in PCE require Agent communication,manual control,and diversity of devices. Hence system in PCE must be designed flexible,and optimize the use of network,storage and computing resources. However,traditional MA software framework cannot completely adapt to these new features. A new MA software framework and its Agent Communication Modules to solve the problem brought by PCE was proposed. To describe more precisely,it presents Wright/ADL (Architecture Description Language) description of the new framework. Then,it displays an application called AI Eleven based on this new framework. AI Eleven achieves Agent-Agent communication and good collaboration for a task. Two experiments on AI Eleven will demonstrate the new framework's practicability and superiority.展开更多
In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorith...In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.展开更多
The tremendous growth of the cloud computing environments requires new architecture for security services. Cloud computing is the utilization of many servers/data centers or cloud data storages (CDSs) housed in many d...The tremendous growth of the cloud computing environments requires new architecture for security services. Cloud computing is the utilization of many servers/data centers or cloud data storages (CDSs) housed in many different locations and interconnected by high speed networks. CDS, like any other emerging technology, is experiencing growing pains. It is immature, it is fragmented and it lacks standardization. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this paper a comprehensive security framework based on Multi-Agent System (MAS) architecture for CDS to facilitate confidentiality, correctness assurance, availability and integrity of users' data in the cloud is proposed. Our security framework consists of two main layers as agent layer and CDS layer. Our propose MAS architecture includes main five types of agents: Cloud Service Provider Agent (CSPA), Cloud Data Confidentiality Agent (CDConA), Cloud Data Correctness Agent (CDCorA), Cloud Data Availability Agent (CDAA) and Cloud Data Integrity Agent (CDIA). In order to verify our proposed security framework based on MAS architecture, pilot study is conducted using a questionnaire survey. Rasch Methodology is used to analyze the pilot data. Item reliability is found to be poor and a few respondents and items are identified as misfits with distorted measurements. As a result, some problematic questions are revised and some predictably easy questions are excluded from the questionnaire. A prototype of the system is implemented using Java. To simulate the agents, oracle database packages and triggers are used to implement agent functions and oracle jobs are utilized to create agents.展开更多
With the development of cloud computing, the mutual understandability among distributed data access control has become an important issue in the security field of cloud computing. To ensure security, confidentiality a...With the development of cloud computing, the mutual understandability among distributed data access control has become an important issue in the security field of cloud computing. To ensure security, confidentiality and fine-grained data access control of Cloud Data Storage (CDS) environment, we proposed Multi-Agent System (MAS) architecture. This architecture consists of two agents: Cloud Service Provider Agent (CSPA) and Cloud Data Confidentiality Agent (CDConA). CSPA provides a graphical interface to the cloud user that facilitates the access to the services offered by the system. CDConA provides each cloud user by definition and enforcement expressive and flexible access structure as a logic formula over cloud data file attributes. This new access control is named as Formula-Based Cloud Data Access Control (FCDAC). Our proposed FCDAC based on MAS architecture consists of four layers: interface layer, existing access control layer, proposed FCDAC layer and CDS layer as well as four types of entities of Cloud Service Provider (CSP), cloud users, knowledge base and confidentiality policy roles. FCDAC, it’s an access policy determined by our MAS architecture, not by the CSPs. A prototype of our proposed FCDAC scheme is implemented using the Java Agent Development Framework Security (JADE-S). Our results in the practical scenario defined formally in this paper, show the Round Trip Time (RTT) for an agent to travel in our system and measured by the times required for an agent to travel around different number of cloud users before and after implementing FCDAC.展开更多
This article deals with the process of formation of norms in societies in which individuals act according to their own cognitive framework under communication. The individuals acquire information from others and inter...This article deals with the process of formation of norms in societies in which individuals act according to their own cognitive framework under communication. The individuals acquire information from others and interpret it. The way of individual’s action is revised through changing the source of information and reforming the method of interpretation. Through the revising mechanisms, assemblages sharing cognitive frameworks are established. First individuals adopt the same source of information and then arrange the shared cognitive framework. The assemblages are considered as groups with common norms, since the same cognitive framework gives actions coherency. In the process of formation the two revising mechanisms function in turn. The intensity of interaction among individuals affects the period to build norms and the size of groups sharing the norms. The size develops under strong interaction but the period becomes long. The dependency of the average size of norms on the strength obeys a power law.展开更多
The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative ...The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative reinforcement learning algorithm is proposed with automatic optimization,namely,Dyna-DQL,to quickly achieve an optimal coordination solution for the multi-area distributed power grids.The proposed Dyna framework is combined with double Q-learning to collect and store the environmental samples.This can iteratively update the agents through buffer replay and real-time data.Thus the environmental data can be fully used to enhance the learning speed of the agents.This mitigates the negative impact of heavy stochastic disturbances caused by the integration of renewable energy on the control performance.Simulations are conducted on two different models to validate the effectiveness of the proposed algorithm.The results demonstrate that the proposed Dyna-DQL algorithm exhibits superior stability and robustness compared to other reinforcement learning algorithms.展开更多
基金Guangdong-Hong Kong Technology Cooperation Funding Scheme, China ( No.2007A010101003)Guangdong-Ministry of Education Industry-University Cooperation Funding Scheme,China (No.2007B090200018)
文摘With the improvement of mobile equipment performance and development of Pervasive Computing,interactive computational applications such as Multi-Agent (MA) systems in Pervasive Computing Environments (PCE) become more and more prevalent. Many applications in PCE require Agent communication,manual control,and diversity of devices. Hence system in PCE must be designed flexible,and optimize the use of network,storage and computing resources. However,traditional MA software framework cannot completely adapt to these new features. A new MA software framework and its Agent Communication Modules to solve the problem brought by PCE was proposed. To describe more precisely,it presents Wright/ADL (Architecture Description Language) description of the new framework. Then,it displays an application called AI Eleven based on this new framework. AI Eleven achieves Agent-Agent communication and good collaboration for a task. Two experiments on AI Eleven will demonstrate the new framework's practicability and superiority.
基金The National Natural Science Foundation of China(No.61300167)the Open Project Program of State Key Laboratory for Novel Software Technology of Nanjing University(No.KFKT2015B17)+3 种基金the Natural Science Foundation of Jiangsu Province(No.BK20151274)Qing Lan Project of Jiangsu Provincethe Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education(No.JYB201606)the Program for Special Talent in Six Fields of Jiangsu Province(No.XYDXXJS-048)
文摘In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.
文摘The tremendous growth of the cloud computing environments requires new architecture for security services. Cloud computing is the utilization of many servers/data centers or cloud data storages (CDSs) housed in many different locations and interconnected by high speed networks. CDS, like any other emerging technology, is experiencing growing pains. It is immature, it is fragmented and it lacks standardization. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this paper a comprehensive security framework based on Multi-Agent System (MAS) architecture for CDS to facilitate confidentiality, correctness assurance, availability and integrity of users' data in the cloud is proposed. Our security framework consists of two main layers as agent layer and CDS layer. Our propose MAS architecture includes main five types of agents: Cloud Service Provider Agent (CSPA), Cloud Data Confidentiality Agent (CDConA), Cloud Data Correctness Agent (CDCorA), Cloud Data Availability Agent (CDAA) and Cloud Data Integrity Agent (CDIA). In order to verify our proposed security framework based on MAS architecture, pilot study is conducted using a questionnaire survey. Rasch Methodology is used to analyze the pilot data. Item reliability is found to be poor and a few respondents and items are identified as misfits with distorted measurements. As a result, some problematic questions are revised and some predictably easy questions are excluded from the questionnaire. A prototype of the system is implemented using Java. To simulate the agents, oracle database packages and triggers are used to implement agent functions and oracle jobs are utilized to create agents.
文摘With the development of cloud computing, the mutual understandability among distributed data access control has become an important issue in the security field of cloud computing. To ensure security, confidentiality and fine-grained data access control of Cloud Data Storage (CDS) environment, we proposed Multi-Agent System (MAS) architecture. This architecture consists of two agents: Cloud Service Provider Agent (CSPA) and Cloud Data Confidentiality Agent (CDConA). CSPA provides a graphical interface to the cloud user that facilitates the access to the services offered by the system. CDConA provides each cloud user by definition and enforcement expressive and flexible access structure as a logic formula over cloud data file attributes. This new access control is named as Formula-Based Cloud Data Access Control (FCDAC). Our proposed FCDAC based on MAS architecture consists of four layers: interface layer, existing access control layer, proposed FCDAC layer and CDS layer as well as four types of entities of Cloud Service Provider (CSP), cloud users, knowledge base and confidentiality policy roles. FCDAC, it’s an access policy determined by our MAS architecture, not by the CSPs. A prototype of our proposed FCDAC scheme is implemented using the Java Agent Development Framework Security (JADE-S). Our results in the practical scenario defined formally in this paper, show the Round Trip Time (RTT) for an agent to travel in our system and measured by the times required for an agent to travel around different number of cloud users before and after implementing FCDAC.
基金a Grant-in-Aid for Scientific Research (No.12780269) from the Ministry of Education,Science, and Culture of Japan. S.E. is sup
文摘This article deals with the process of formation of norms in societies in which individuals act according to their own cognitive framework under communication. The individuals acquire information from others and interpret it. The way of individual’s action is revised through changing the source of information and reforming the method of interpretation. Through the revising mechanisms, assemblages sharing cognitive frameworks are established. First individuals adopt the same source of information and then arrange the shared cognitive framework. The assemblages are considered as groups with common norms, since the same cognitive framework gives actions coherency. In the process of formation the two revising mechanisms function in turn. The intensity of interaction among individuals affects the period to build norms and the size of groups sharing the norms. The size develops under strong interaction but the period becomes long. The dependency of the average size of norms on the strength obeys a power law.
基金supported by the National Natural Sci-ence Foundation of China(No.52277108)Guangdong Provincial Department of Science and Technology(No.2022A0505020015).
文摘The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative reinforcement learning algorithm is proposed with automatic optimization,namely,Dyna-DQL,to quickly achieve an optimal coordination solution for the multi-area distributed power grids.The proposed Dyna framework is combined with double Q-learning to collect and store the environmental samples.This can iteratively update the agents through buffer replay and real-time data.Thus the environmental data can be fully used to enhance the learning speed of the agents.This mitigates the negative impact of heavy stochastic disturbances caused by the integration of renewable energy on the control performance.Simulations are conducted on two different models to validate the effectiveness of the proposed algorithm.The results demonstrate that the proposed Dyna-DQL algorithm exhibits superior stability and robustness compared to other reinforcement learning algorithms.
