智能代理技术在网络集成中的应用

目前,企业内部的各类信息之间不能实现共享已经成为了急待解决的问题。本文提出了一种利用智能代理进行网络集成的方法,较好的解决了上述问题。并以华北石油电网水网实时数据联网信息服务系统为背景,分析了智能代理在网络集成中的地位和作用,智能代理的逻辑结构各部分的实现技术。     

论智能代理在网络教学中的应用

虽然网络教学方便了学习者到达世界的任何一个角落，但现有网络教学也有其限制和缺点。智能代理正好克服了当今网络教学的缺点。本文首先论述了国内外智能代理的教学应用研究动态，然后分析了网络教学系统的现状，最后阐述了智能代理在网络教学中的应用。     

智能代理应用于网络教学的基本构想

随着越来越多的学校开展网络教学，支持网络教学软件也越来越多，但教学资源的配置并没有因网络的存在而最大程度地趋向合理。本文通过提出把智能代理技术引入教学系统，帮助教师对学生学习过程实施迅速高效的检查，而教师则可以在“八”的人层面上充分发挥效力；通过学生端的代理模块,帮助学生完成学习材料的收集与虚拟学伴和教师的交流，另外通过教学代理给学生及时的引导和建议，从而实现适应性学习。     

加强学习算法的智能多代理在远程教学中的应用

基于WWW的远程教学系统可以被多个用户同时访问,并且系统内部一般都包含多个独立教学模块,因此很适合于用人工智能领域中的多代理技术来刻画,文章在研究基于加强学习算法的多代理系统基础上,设计开发了一种基于智能多代理技术的远程教学模型,此模型能够实现基于WWW的远程教学系统的智能性和自适应性。     

智能决策支持系统的一种学习推理机

介绍几种软计算技术，并利用软计算融合技术，提出一种新的智能决策支持系统的学习推理系统。该学习推理系统可以用作智能决策支持系统的学习模块，由此可以生成智能决策支持系统的知识库。     

一种基于代理者团体的工艺设计思想

本文介绍了一个基于代理者团体的工艺设计系统，着重分析了如何在现有ＣＡＰＰ系统基础上找出代理者并建立代理者团体。该系统能够在分布式数据库和推理机的基础上推理生成符合生产实际的柔性工艺。     

智能对象语言CLOS的分布实现

ＣＬＯＳ系统是一个嵌入Ｃｏｍｍｏｎ Ｌｉｓｐ的面向对象标准语言。文中结合所提出的类分方法，通过引入同步或异步通信协议和ＲＰＣ并发控制，详细介绍了一个新的分布ＣＬＯＳ系统ＰａｒＣＬＯＳ。     

基于对象的软件代理的设计和实现

在对代理和对象技术进行了分析对比的基础上,设计了一种基于对象的软件代理,不仅具有代理的优势而且充分利用目前的对象技术。学给出了基于对象的软件代理系统的实现技术及其对敏捷供应链的解决问题的应用实例。     

一种基于代理思想的软件分析模型

Agent概念来自人工智能领域，讨论了Agent思想在软件工程中的应用，提出了一种基于Agent思想的软件分析一般模型，并在该模型的基础上提出了一套分析方法，该模型与方法发展了面向对象技术，且更适于分析包含复杂任务的系统。     

面向分布式对象的一种集成服务报表系统

结合一个分布式网络下报表系统设计实例,提出了基于服务的分布式软件设计思想,采用将报表客户端以及报表服务包装成CORBA对象并通过数据服务与分布式数据源进行通信的方案。并就远程报表系统的设计与实现以及数据服务的具体问题进行了论述,给出了程序流程和设计要点。     

基于Agent的协同远程教学模型

利用网络进行远程教学有很多优点。文章讨论如何反Agent和计算支持的协同工作技术应用到远程教学中以增强教学效果。在多Agent通信语言KQML的基础上，提出了基于Agent的协同远程教学模型，并阐述了在此模型下协同远程教学的工作过程。     

智能代理技术在CAI系统中的应用

计算机多媒体技术的发展为计算机辅助教学(Computer-Assisted Instruction ,CAI)带来了'广阔的发展空间,推出了一大批计算机辅助教学软件,为教育事业的发展带来了积极影响,当前,如何将新的教育理念融入于计算机辅助教学是一项极具挑战性的工作.通过对一些传统计算机辅助教学系统的研究,指出教育理念的落后,缺乏智能化是目前计算机辅助教学系统水平偏低的主要原因之一,为此引入了新的教育理论--建构主义思想,并在此基础上应用智能代理技术,提出了智能计算机辅助教学系统开发框架,应用此框架能使计算机辅助教学系统更加人性化,智能化,真正发挥其作用.     

Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool

This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool.     

基于移动Agent的智能家庭远程监控系统设计

提出了一种基于移动Agent计算模式的智能家庭远程监控系统实现方案。该方案充分利用了移动代理计算的优点,为智能家庭远程监控的时实和可靠的实现提供了一种新途径。系统在应用中显示了良好的控制效果和运行稳定性。     

基于KQML和XML的多Agent系统:NKMAS

文章介绍一个基于KQML和XML的多Agent系统———NanKaiMultiAgentSystem(NKMAS)的设计和实现,详细描述了NKMAS的内部结构以及运行机制。     

Intelligent welding system technologies: State-of-the-art review and perspectives

Welding systems are being transformed by the advent of modern information technologies such as the internet of things, big data, artificial intelligence, cloud computing, and intelligent manufacturing. Intelligent welding systems (IWS), making use of these technologies, are drawing attention from academic and industrial communities. Intelligent welding is the use of computers to mimic, strengthen, and/or replace human operators in sensing, learning, decision-making, monitoring and control, etc. This is accomplished by integrating the advantages of humans and physical systems into intelligent cyber systems. While intelligent welding has found pilot applications in industry, a systematic analysis of its components, applications, and future directions will help provide a unified definition of intelligent welding systems. This paper examines fundamental components and techniques necessary to make welding systems intelligent, including sensing and signal processing, feature extraction and selection, modeling, decision-making, and learning. Emerging technologies and their application potential to IWS will also be surveyed, including Industry 4.0, cyber-physical system (CPS), digital twins, etc. Typical applications in IWS will be surveyed, including weld design, task sequencing, robot path planning, robot programming, process monitoring and diagnosis, prediction, process control, quality inspection and assessment, human-robot collaboration, and virtual welding. Finally, conclusions and suggestions for future development will be proposed. This review is intended to provide a reference of the state-of-the-art for those seeking to introduce intelligent welding capabilities as they modernize their traditional welding stations, systems, and factories.     

A Wireless Instructor System for Computer-Supported Collaborative Learning Requiring Immersive Presence (CSCLIP)

The Computer-Supported Collaborative Learning Requiring Immersive Presence (CSCLIP) concept has been established with the objective of extending and enhancing thee-learning experience of distance education, especially for classes that involve laboratory (lab) experiments. The CSCLIP concept defines immersive presence as an inherent requirement that enables cognitive, affective, and most importantly psychomotor learning objectives to integrate into designs and concepts for next generation e-learning systems (Sharda et~al., 2003). Within the CSCLIP architectural framework, the Wireless Instructor (WI) system has been conceptualized and developed as an essential device to effectively support teaching while roaming instructional features for both local and distance students. The WI system provides cost effective means to establish a real-time immersive presence for the distance learning (DL) student and his/her lab group peers. The technical design and system architecture to create a WI system are introduced in this paper. The objective of the WI system is to make the learning experience more vivid and interactive by enabling the DL students, as well as the local students that are not in the same room with the instructor(s) at the same time, to be able to flexibly interact with the instructor(s) in real-time. With this system the students can experience real-time or non-real-time virtual tours with the instructor(s), enabling the students to visit places that may not be easily accessible due to distance, limited space and/or time, cost, or possible danger. The WI system consists of two major sub-components. First is a wireless audio and video (AV) system, which transfers real-time AV signals to and from the instructor(s) to all students. Second is the wireless instructor locator & data assistant system. These two systems can be combined into one WI unit, but as the applied development technologies are somewhat distinct, their features and architectural designs will be described separately throughout this paper. Integration of the two systems will enable further capabilities.This revised version was published online in March 2005 with corrections to the cover dateThis project was funded by the U.S. Department of Education (DoE) award no. P116Z020042 project titled Telecommunications Virtual Laboratory Development.     

HED-FL: A hierarchical,energy efficient,and dynamic approach for edge Federated Learning

The increasing data produced by IoT devices and the need to harness intelligence in our environments impose the shift of computing and intelligence at the edge, leading to a novel computing paradigm called Edge Intelligence/Edge AI. This paradigm combines Artificial Intelligence and Edge Computing, enables the deployment of machine learning algorithms to the edge, where data is generated, and is able to overcome the drawbacks of a centralized approach based on the cloud (e.g., performance bottleneck, poor scalability, and single point of failure). Edge AI supports the distributed Federated Learning (FL) model that maintains local training data at the end devices and shares only globally learned model parameters in the cloud. This paper proposes a novel, energy-efficient, and dynamic FL-based approach considering a hierarchical edge FL architecture called HED-FL, which supports a sustainable learning paradigm using model parameters aggregation at different layers and considering adaptive learning rounds at the edge to save energy but still preserving the learning model’s accuracy. Performance evaluations of the proposed approach have also been led out considering model accuracy, loss, and energy consumption.     

平行高特:基于ACP的平行痛风诊疗系统框架*

为了解决复杂环境中痛风诊疗的精准决策难题,突破不同医生业务水平对于痛风诊疗的局限,提高痛风诊断的准确率和治疗的有效性,文中提出基于ACP理论的平行痛风诊疗系统框架,称为“平行高特(Gout)”.平行高特通过构建人工痛风诊疗系统以模拟和表示实际痛风诊疗系统,运用计算实验进行各种痛风诊疗模型的训练与评估,借助平行执行对实际痛风诊疗系统进行管理决策与实时优化,实现痛风诊疗过程的自动化与智能化.该平行的诊疗过程可以帮助医生减少误诊误治,提高效率,提升水平,同时也能帮助患者做好慢病管理,远离疾病.考虑到痛风病在当前社会的严重程度,平行高特在痛风诊疗中的应用具有重要的实际意义,是传统医疗模式走向智慧化、平行化的有效途径和自然选择,有利于推进健康中国建设,实现更高水平的全民健康.     

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed.