Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

The dramatic improvement of information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. This paper discusses how Artificial Intelligence and Machine Learning techniques are adopted to fulfill users’ needs in a social learning management system named “CourseNetworking”. The paper explains how machine learning contributed to developing an intelligent agent called “Rumi” as a personal assistant in CourseNetworking platform to add personalization, gamification, and more dynamics to the system. This paper aims to introduce machine learning to traditional learning platforms and guide the developers working in LMS field to benefit from advanced technologies in learning platforms by offering customized services.

Research and Design of Network Management Construction Based on Mobile AGENT

The technical characters of mobile agent （MA） originated in the distributional artificial intelligence domain is introduced. A network management construction based on agent （NMCA） is then proposed. The NMCA structure features are elaborated in detail. A prototype design of NMCA is given by using the jKQML programming. The establishment of NMCA platform will be helpful to reduce the correspondence load of network management and improves the efficiency and the expansion ability of network management systems.

Design and Implementation of a Network Management System Based on Mobile RMON Agent

To improve the scalability of RMON-based network management, the concept of Mobile RMON Agent (MRA) was presented by combining the mobile agent technology with RMON. Then an MRA-based Network Management System (MRANMS) was designed and implemented. RMON functions were achieved by programming the mobile agent to make it capture and analyze packets locally. The experimental result on the Grasshopper platform indicates that MRA as a mobile agent can migrate to another subnet and calculate a RMON MIB value and TrafficMatrix in a subnet with 29 hosts. Furthermore, the behavior of MRA can be customized to achieve new RMON functions, such as the statistics of hostTable in RMON MIB. It can be concluded that MRANMS is based on the mode of distribution, and besides the compatibility with standard RMON, the system possesses scalability of management function.

Introducing Intelligent Agents Potential into a competent Integral Multi-Agent Sensor Network Simulation Architecture Design

During this research we spot several key issues concerning WSN design process and how to introduce intelligence in the motes. Due to the nature of these networks, debugging after deployment is unrealistic, thus an efficient testing method is required. WSN simulators perform the task, but still code implementing mote sensing and RF behaviour consists of layered and/or interacting protocols that for the sake of designing accuracy are tested working as a whole, running on specific hardware. Simulators that provide cross layer simulation and hardware emulation options may be regarded as the last milestone of the WSN design process. Especially mechanisms for introducing intelligence into the WSN decision making process but in the simulation level is an important aspect not tackled so far in the literature at all. The herein proposed multi-agent simulation architecture aims at designing a novel WSN simulation system independent of specific hardware platforms but taking into account all hardware entities and events for testing and analysing the behaviour of a realistic WSN system. Moreover, the design herein outlined involves the basic mechanisms, with regards to memory and data management, towards Prolog interpreter implementation in the simulation level.

Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense effectiveness.Therefore,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense strategies.the designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

Light-Weight Simple Network Management Protocol in Wireless Sensor Networks

A Light-Weight Simple Network Management Protocol (LW-SNMP) for the wireless sensor network is proposed, which is a kind of hierarchical network management system including a sink manager, cluster proxies, and node agents. Considering the resource limitations on the sensor nodes, we design new management messages, new data types and new management information base completely. The management messages between the cluster proxy and node agents are delivered as normal data packets. The experiment results show that LW-SNMP can meet the management demands in the resource-limited wireless sensor networks and has a good performance in stability, effectiveness of memory, extensibility than the traditional Simple Network Management Protocol (SNMP).

Knowledge-Based Network Management System for Movable and Deployable ICT Resource Unit

When a disaster occurs, the demand for information and communication technology (ICT) services drastically increases. To meet such demands, a national project was undertaken in Japan to develop the Movable and Deployable ICT Resource Unit (MDRU). One challenge regarding the MDRU is securing operators to work the units in emergency situations. As ICT service users have diverse and frequently changing demands, strong technical skills and practical knowledge are required for the administration of MDRUs. In this paper, we propose a knowledge-based network management system to alleviate the burden on administrators. To deal with the structural changes to network systems that frequently occur with changes in ICT service demand, we introduce modularization techniques into our previous research. The proposed system can be easily reconfigured by join/disjoin modules corresponding to changes in the system configuration of the MDRU. The results of our experiments using the implemented experimental system confirm that the proposed system can be applied to MDRU operation and effectively supports administrators.

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

An Intelligent Agent Approach to Adaptive Project Management

This paper presents the initial steps to integrating a distributed discrete event simulation system with a framework for intelligent software agents. The resulting system has a simulation component that is based on the high-level architecture (HLA) and an agent component that implements the belief-desire-intention (BDI) approach to agent modelling. The architecture is connected to a real-time information source. The framework was successfully applied to a real-life monitoring system for a tunnel-boring machine excavation project that helped with forecasting and managing the project timelines in response to the changes in the uncertain excavation environment. This project is presented as a test case and demonstrates encouraging results for integrative modelling of large-scale problems with elements of uncertainty.

Research and Implementation of Network Intelligent Management Based on CSCW

This paper analyzes progresses and difficulties of subjects on computer network’s management and artificial intelligence, proposes AGIMA, a new model of network intelligent management, which is based on computer supported cooperative work (CSCW) and combining new technologies such as WWW, Java. AGIMA transfers from information distribution centered mode in traditional network management to computing distribution centered mode, providing intelligence capacity for network management by a whole intelligent agent group. The implementation of AGIMA takes much consideration of openess, scalability, proactive adaptability and friendliness of human computer interface. Authors present properties of intelligent agent in details, and conclude that network intelligence should be cooperation between human and computer.

基于Agent人工智能的异构网络多重覆盖节点入侵检测系统设计

异构网络具有结构复杂、多重覆盖面积大等特征,使得网络入侵检测较为隐蔽,威胁网络运行的安全性;为此,对基于Agent人工智能的异构网络多重覆盖节点入侵检测系统进行了研究;通过检测Agent和通信Agent装设主机Agent,以Cisco Stealthwatch流量传感器作为异构网络传感器检测攻击行为,采用STM32L151RDT664位微控制器传输批量数据,由MAX3232芯片实现系统电平转化,实现硬件系统设计;软件部分设计入侵检测标准,采用传感器设备捕获网络实时数据,通过Agent技术解析异构网络协议并提取数据运行特征,综合考虑协议解析结果及与检测标准匹配度,实现异构网络多重覆盖节点入侵检测;经实验测试表明,基于Agent人工智能的异构网络多重覆盖节点入侵检测系统入侵行为的漏检率和入侵类型误检率的平均值仅为6%和5%,能够有效提高检测精度,减小检测误差。

Optimization Management of Industrial Organizations Based on Performance Indicators

This paper proposes an intelligent management system (IMS) to help managers in their delicate and tedious task of exploiting the plethora of data (indicators) contained in management dashboards. This system is based on intelligent agents, ontologies and data mining. It is implemented by PASSI (Process for Agent Societies Specification and Implementation) methods for agent design and implementation, the Methodology for Knowledge Modeling and Hot-Winters for data prediction. Intelligent agents not only track indicators but also store the knowledge of managers within the company. Ontologies are used to manage the representation and presentation aspects of knowledge. Data mining makes it possible to: make the most of all available data;model the industrial process of data selection, exploration and modeling;and transform behaviors into predictive indicators. An instance of the IMS named SYGISS, currently in operation within a large brewery organization, allows us to observe very interesting results: the extraction of indicators is done in less than 5 minutes whereas manual extraction used to take 14 days;the generation of dashboards is instantaneous whereas it used to take 12 hours;the interpretation of indicators is instantaneous whereas it used to take a day;forecasts are possible and are done in less than 5 minutes whereas they did not exist with the old management. These important contributions help to optimize the management of this organization.

Study of the Security in Network Management Based on Mobile Agents

Security of mobile-agent based network management must be considered due to the widespread adoption of mo-bile agents in network management,which involves the protections of mobile agents,management station and managed de-vices.The integrated security model proposed in our paper incorporates the effective security countermeasures of these entitiesinto a trusted execution environment and two security protection layers.Sandbox based on Java virtual machine and Javacard independent on the network devices also with the cryptography technology in this model together protect the networkmanagement process.

Research on tasks schedule and data transmission of video sensor networks based on intelligent agents and intelligent algorithms

As a novel application technology,wireless video sensor networks become the current research focus,especially on target tracking and surveillance scenario.Based on multiple agents＇ technique,this article introduces a series of intelligent algorithms such as simulated annealing algorithm（SA）,genetic algorithm（GA）,and ant colony optimization algorithm（ACO） or their mixed algorithms,to resolve the optimization of tasks schedule and data transmission.This article analyzes the performance of abovementioned algorithms and verifies their feasibility associated with agents.The simulations demonstrates that the mixed algorithms based on SA and GA obtain the optimal solution to tasks schedule,and those combined with SA-ACO show advantages on multimedia sensor networks routing optimization.

Intelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems

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.

AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

Intelligent Load Management Scheme for a Residential Community in Smart Grids Network Using Fair Emergency Demand Response Programs

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modelling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent load management scenario.

基于多Agent的含DG智能配电网差动保护自愈方法

含DG智能配电网中的潮流方向随分布式发电的变化而变化,导致配电网故障自愈的效果难以保障,因此,提出基于多Agent的含DG智能配电网差动保护自愈方法。通过构建基于多Agent的MAS体系结构,将其应用于含DG智能配电网差动保护和自愈的控制,在MAS体系结构的数据处理层完成差动保护,并在MAS体系结构的协调控制层实现故障自愈。结果表明,该设计方法不仅可以恢复配电网故障区域的电力,而且在操作次数、线损及功率因数方面可达到较理想的效果。

FEDRP Based Model Implementation of Intelligent Energy Management Scheme for a Residential Community in Smart Grids Network

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modeling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent energy management scenario.