An Intelligent Agent Based on Virtual Geographic Environment System

On the basis of previous work, this paper designs an intelligent agent based on virtual geographic environment (VGE) system that is characterized by huge data, rapid computation, multi\|user, multi\|thread and intelligence and issues challenges to traditional GIS models and algorithms. The new advances in software and hardware technology lay a reliable basis for system design, development and application.

Intelligent Agent-Based System for Digital Library Information Retrieval

A new information search model is reported and the design and implementation of a system based on intelligent agent is presented. The system is an assistant information retrieval system which helps users to search what they need. The system consists of four main components: interface agent, information retrieval agent, broker agent and learning agent. They collaborate to implement system functions. The agents apply learning mechanisms based on an improved ID3 algorithm.

Coordinated dynamic mission planning scheme for intelligent multi-agent systems

Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor systems.However,it still faces challenges due to the system complexity,the execution order constraints,and the dynamic environment uncertainty.To address it,a coordinated dynamic mission planning scheme is proposed utilizing the method of the weighted AND/OR tree and the AOE-Network.In the scheme,the mission is decomposed into a time-constraint weighted AND/OR tree,which is converted into an AOE-Network for mission planning.Then,a dynamic planning algorithm is designed which uses task subcontracting and dynamic re-decomposition to coordinate conflicts.The scheme can reduce the task complexity and its execution time by implementing real-time dynamic re-planning.The simulation proves the effectiveness of this approach.

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.

Design of an Intelligent Self-Healing Smart Grid using a Hybrid Multi-Agent Framework

This paper discusses the applications of a hybrid multi-agent framework for self-healing applications in an intelligent smart grid system following catastrophic disturbances such as loss of generators or during system fault.The proposed hybrid multi-agent framework is a hybrid of both centralized and decentralized scheme to allow distributed intelligent agent in the smart grid system to make fast local decision while allowing the slower central controller to judge the effectiveness of the decision made by the local agents and to suggest more optimal solutions.

Integration of Agent System with Legacy Software

Agent technique is a new method that can analyze, design and realize a distributed open system. It has been used in almost every field. But if act for the real practical words in technique, it must integrate with legacy software, such as database system etc, and control them. This paper introduces the specification of agent software integration, ontology, instances database as implementing agent software integration with CORBA technique and takes XML, ACL as language communicating among agents.

Multi-agent Systems and Cloud Computing for Controlling and Managing Chemical and Food Processes

Cloud computing can offer a very powerful, reliable, predictable and scalable computing infrastructure for the execution of MAS （multi-agent systems） implementing complex agent-based applications such when modelling, simulation and real-time running of complex systems must be provided. Multi-agent systems appears as an adequate approach to current challenges in many areas. Between important qualities of MAS also belongs to, that they are open, interoperable, and heterogenous systems. The agent is active, a program entity, has its own ideas how to perform the tasks of the own agenda. Agents： perceive, behave ＂reasonably＂, act in the environment, communicate with other agents. Cloud infrastructures can offer an ideal platform where run MAS systems simulations, applications and real-time running because of its large amount of processing and memory resources that can be dynamically configured for executing large agent-based software at unprecedented scale. Cloud computing can help chemical and food companies drive operational excellence; meet growing and changing customer demands; accelerate new product innovation and ramp-to-volume manufacturing in key markets; reduce IT spending; manage and mitigate supply chain risks; and enable faster and more flexible delivery of new IT system. Production type of SOC （service-oriented computing） can be inspired by a ＂Cloud＂, for the production of ＂Cloud＂ offers an attractive and natural solutions in several computing trends such as delivery system over the Internet, use of utilities, flexibility, virtualization, a ＂grid＂ distributed computing, outsourcing, Web 2.0, etc.. Production of the ＂Cloud＂ is also considered as a new multidisciplinary field that includes ＂network＂ production, virtual manufacturing, agile manufacturing, and of course cloud computing. Examples of cloud computing and MAS applications in food and chemistry development and industry, proposition of using multi-agent systems in the control of batch processes, modified ACO （ant colony optimization） approach for the diversified service allocation and scheduling mechanism in cloud paradigma, examples of applications in a business area were studied in the paper.

MULTI-AGENT COMPUTER AIDED ASSEMBLY PROCESS PLANNING SYSTEM FOR SHIP HULL

A multi agent computer aided assembly process planning system (MCAAPP) for ship hull is presented. The system includes system framework, global facilitator, the macro agent structure, agent communication language, agent oriented programming language, knowledge representation and reasoning strategy. The system can produce the technological file and technological quota, which can satisfy the production needs of factory.

Design and Application of an Intelligent Agent for Web Information Discovery

With the propagation of applications on the internet, the internet has become a great information source which supplies users with valuable information. But it is hard for users to quickly acquire the right information on the web. This paper an intelligent agent for internet applications to retrieve and extract web information under user's guidance. The intelligent agent is made up of a retrieval script to identify web sources, an extraction script based on the document object model to express extraction process, a data translator to export the extracted information into knowledge bases with frame structures, and a data reasoning to reply users' questions. A GUI tool named Script Writer helps to generate the extraction script visually, and knowledge rule databases help to extract wanted information and to generate the answer to questions.

Optimizing Alzheimer’s Disease Therapy Using a Neural Intelligent Agent-Based Platform

Alzheimer’s disease affects millions of persons every year. Negative emotions such as stress and frustration have a negative impact on memory function and Alzheimer's patients experience more negative emotions than healthy adults. Non-pharmacological treatment such as immersion in virtual environments could help Alzheimer patients by reducing their negative emotions, but it has restrictions and requirements. In this work, we present three virtual reality relaxing systems in which the patients are immersed in relaxing environments. We propose to use intelligent agents in order to adapt the relaxing environment to each participant and optimize its relaxation effect. The intelligent agents track the emotions of patients using electroencephalography as input in order to adapt the environments. We designed each system with different levels of intelligence in order to analyze the impact of the adaptation on the patients. Experiments were performed for each system on participants with subjective cognitive decline. Results show that these relaxing systems can reduce negative emotions and improve participants’ memory performance. The positive effects on affective state and memory persisted for a longer period of time and were generally more effective for the systems with more intelligence. We believe that the combination of a relaxing environment, virtual reality, intelligent agents for adapting the environment, and brain assessment is a promising method for helping Alzheimer’s patients.

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

An Intelligent Agency Framework to Realize Adaptive System Management

In order to realize the required scalable and adaptive system management, an interactive intelligent agency framework, iSMAcy （intelligent System Management Agency） , is proposed as an integrated solution to realize distributed autonomoas system management. Firstly, it is a multiagent platform that supports inter-agent communication and cooperation. Secondly, the functional agents are based on intentional agent architecture that achieves balance between goal-directed behavior and situated reactive action. An example of applying the iSMAcy system to a network management environment has been described to illustrate and validate the scalable and adaptive management capability of the intelligent agency framework.

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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.

关于采用AGENT技术构建远程教育智能导学系统的研究

介绍 AGENT技术的概念与特点 ,并应用 AGENT技术创建远程教育智能导学系统的基本框架 ,给出 AGENT结构图和导学策略及自学方案自动生成流程图 ,阐述系统的总体设计思路和智能化特征。指出 AGENT技术应用于远程教育网络对解决异质计算环境的协调问题 ,实现主动的智能化信息服务和提供有针对性。

多Agent在电子商务系统中的应用

针对当前开展电子商务所面临的信息量过载及电子商务交易过程中繁琐的交易细节等问题，提出了多Agent技术解决的方法及解决技术，并研究了多Agent的性能、特点及在电子商务系统中的具体应用，论证了应用JADE平台创建多Agent系统的可行性．

e-Automation,an Architecture for Distributed Industrial Automation Systems

Due to the complexity of modern industrial systems, a conventional automation system is not capable of providing sufficient information management and high-level intelligent approaches, as achieving these functionalities requires the support of comprehensive data management and coordination between system devices and heterogenous information. This paper proposes the concept of e-Automation, in which computer networking and distributed intelligence agent technologies are applied to industrial automation systems, and presents a hardware and software architecture that implements this concept. An open infrastructure based on multi-agent systems is employed in the proposed architecture of e-Automation, which aims to allow the implementation of diverse tasks and to permit greater configurability than can be obtained from a traditional system. To evaluate our proposed e-Automation concept, this paper presents a case study of substation information management which adopts the proposed e-Automation architecture in power system domain.

An Integrated Information Retrieval Support System for Campus Network

This paper presents a new integrated information retrieval support system （IIRSS） which can help Web search engines retrieve cross-lingual information from hereto geneous resources stored in multi-databases in Intranet. The IIRSS, with a three-layer architecture, can cooperate with other application servers running in Intranet. By using intelligent agents to collect information and to create indexes on the-fly, using an access control strategy to confine a user to browsing those accessible documents for him/her through a single portal, and using a new cross-lingual translation tool to help the search engine retrieve documents, the new system provides controllable information access with different authorizations, personalized services, and real-time information retrieval.

Research on Negotiating Agent Development

The paper presents a flexible and effective method of development of negotiating agents. A strategy specification, which is specified by a state chart and defeasible rules, can be dynamically inserted into an agent shell incorporating a state chart interpreter and a defeasible logic inference engine, in order to yield a desirable agent. The set of desirable criteria and rules is required to be justified with different context of the application. Key words automatic negotiation - intelligence agent - defeasible logic - state chart CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60173051)Biography: WEI Ding-guo (1964-), male, Associate professor, research direction: computer network protocal etc.

Advanced Industrial Engineering in Next Generation of Manufacturing Systems

The article deals with possible approaches to the development trends in the industrial engineering in manufacturing organizations. The authors emphasize the need for integration of advanced industrial engineering in the next generation of manufacturing systems, which responds to new trends of production, innovation and advanced technology. This integration represents a sustainable development, so that humanization of work are increased, more effective use of natural and energy resources are achieved and production costs are reduced. Trends in the products manufacturing must meet both industrial engineering as well as production management. The development trends in the industrial engineering in manufacturing organizations must use methods and tools of advanced industrial engineering to achieve competitiveness. The second part of this article deals with specification of these approaches in next generation of production systems.

A New Approach of Intelligent Data Retrieval Paradigm

What is a real time agent,how does it remedy ongoing daily frustrations for users,and how does it improve the retrieval performance in World Wide Web?These are the main question we focus on this manuscript.In many distributed information retrieval systems,information in agents should be ranked based on a combination of multiple criteria.Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness.Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined.The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination.Different ways for ranking agents make this strategy difficult.In this research,we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time.The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes.The proposed approach was tested on the queries provided by Text Retrieval Conference(TREC).Experimental results showed that our approach is effective and robust compared with offline search platforms.