基于自组织社区发现协作学习算法

远程学习者通常很难判断哪些学习资源最适合他们的阅读需要,同时对教师来说,针对每个学习者重新组织不同的学习资源几乎是不可能的.基于此,提出一种新颖的学习偏好建模方法,通过将动态学习数据映射为"资源、评估"的方式实现对学习特征的综合评估;通过构建智能代理来监控学习者的动态学习行为;提出组隶属度奖励机制和组成员交换机制,实现对分布式环境下的相似学习者的社区自组织;同时,基于JADE智能代理平台开发了一个协作学习平台,使得具有相似学习偏好的学习者能够进行学习资源和经验的共享.实验证明,算法具有较高的匹配准确性和社区构建效率,并能够切实提高协作学习的有效性.     

互惠社区构建及个性化推荐算法仿真研究

社区构建对于降低个性化推荐的计算复杂度、提高推荐预测准确度具有重要的意义.该文提出了一种基于组代理的互惠社区构建及个性化推荐算法并进行了仿真实现,着重对分布式环境下的用户数据管理、相似用户查找和社区自组织的问题进行了研究.首先对基于组代理的互惠社区的仿真模型进行了描述,然后引入向量空间模型,并综合不同资源的特征频度向量和用户对该资源的评估值构成用户偏好特征向量（IFV,Interest Feature Vector）,作为衡量用户兴趣一致性和相似用户的匹配的标准,并在此基础上提出组隶属度和推荐认可等机制来实现社区结构的动态调整,试验证明该算法具有较高的社区构建效率和推荐准确度,并具有良好的可扩展性.     

基于JADE的学习网络构建系统研究与实现

针对远程教育环境中,学习者分散、缺乏个性化学习指导等问题,构建了一个基于JADE的学习网络与个性化学习系统.系统为每个学习者创建一个JADE代理,用以动态监控学习行为并实现感兴趣资源的共享、推荐和评估,同时基于其他学习者代理对不同资源的感兴趣程度,通过发现相似性、更新信任权值和调整潜在邻居等方法,动态调整学习者之间的信任关系,构建学习网络,为远程学习者提供更准确地学习资源推荐.实验结果表明系统可以非常迅速的将具有相同兴趣的学习者聚合在一起,并很好的满足他们的查询、推荐需求.     

一种自组织的虚拟学习社区构建算法

远程教育已趋向于网络化,而学习者之间如何进行协作式学习已成为一个关键问题。本文提出一种自组织的学习社区构建算法。该算法是基于学习者的兴趣,将具有相似兴趣的学生自动组成学习社区,以进行协作式学习。实验证明,本算法具有较高的效率和良好的扩展性。     

基于E-Learning的社区监控及个性化推荐系统的实现

针对我国E-Learning学生规模大、交流困难、学习兴趣及能力参差不齐的特点,基于现有的E-Learning学习平台,提出了一种新颖的基于学习经验案例的E-Learner档案规范定义,构建了一个学习社区监控及个性化推荐系统,帮助具有相似学习偏好和学习状态的学生能够相互推荐资源、共享学习经验,真正实现社区用户的协同学习和个性化推荐.     

基于SNS的Web协作学习系统设计

随着Web2.0的快速发展,具有社会特征的SNS软件不断涌现,基于SNS平台的Web协作学习逐渐成为一种新型学习模式。提出了一种基于SNS的Web协作学习模式,并结合国内主流的SNS平台进行二次开发,设计了一个面向SNS的Web协作学习系统,主要实现了个人社区、协作学习社区、文件共享和问答系统等模块。实践证明该系统能够实现高效的Web协作和资源共享,能够帮助学习者更好地交流学习心得和分享学习成果,从而增强学习者的学习兴趣和解决问题的能力。     

个性化小学英语学习系统的设计与实现

针对目前资源学习系统缺乏个性化导致小学英语学习者的资源选择迷航问题,构建以个性化资源组织为核心的学习系统。通过纪录用户信息和个性化学习行为,建立小学英语学习者信息模型;以知识点标注的方式描述英语学习资源,建立学习资源库;运用学习偏好算法和学习水平算法计算学习者偏好,采用新型智能推荐技术,向用户推荐个性化的学习资源。通过原型系统运行实例,其结果验证了个性化学习和智能推荐的有效性。     

一种基于兴趣相似度的学习社区算法

远程教育为学生和教师提供了巨大的方便,但同时也导致了许多“孤独“的学习者.提出一种基于兴趣相似度的学习型社区算法.该算法是基于学习者的兴趣,将具有相似兴趣的学生自动组成学习社区,进行协作式学习.实验证明,本算法具有较高的效率和良好的扩展性.     

基于虚拟学习社区的智能学习算法研究

随着信息技术和互联网技术的发展,虚拟学习社区为学习者提供一个开放的学习环境,是满足时代教育要求的强有力的平台,成为学习者网络交流、网络学习的重要手段。如何利用虚拟学习社区来提高学习者的学习效率成为当前研究的热点和重点。本文基于虚拟学习社区,提出一种智能的个性化推荐学习算法,使学习者能够方便、有效、智能、反思性学习,为构建虚拟学习社区提供理论技术支撑,以便更好地指导虚拟学习社区的学习实践。     

基于人工智能和元宇宙的人机交互学习智能代理

文章提出一种基于人工智能和元宇宙的人机交互学习智能代理,将佛教经典卷宗——《心经》中的模糊本体论应用于人工智能(artificial intelligence, AI)中,使得中小学生在AI-FML元宇宙中学习英语和模糊标记语言(fuzzy markup language, FML);计算智能是人工智能的一个重要分支,它包括模糊逻辑、神经网络和进化计算;基于强化学习、智能代理、本体和AI-FML人机交互学习技术,提出了一种基于模糊本体的智能代理,供学生在AI-FML元宇宙平台上体验计算智能应用,学习模糊逻辑、神经网络和进化计算的基本概念;将模糊本体和AI-FML的核心技术与《心经》相结合,在AI-FML元宇宙中构建具有计算智能、感知智能和认知智能的智能代理;此外,还运用人类对《心经》的语义理解,将AI-FML元宇宙平台的概念解释给中小学生学习和体验计算智能;智能代理与学习环境和AI-FML元宇宙进行交互,可以帮助教师评估学生们在每个课时学习英语或计算智能应用的表现,并以热力图的形式来反映;通过评估AI-FML元宇宙在上海4所国际中小学的实施情况,观察到智能代理能够有效工作,学生的学习成...     

Using Dialogue Features to Predict Trouble During Collaborative Learning

A web-based, collaborative distance-learning system that will allow groups of students to interact with each other remotely and with an intelligent electronic agent that will aid them in their learning has the potential for improving on-line learning. The agent would follow the discussion and interact with the participants when it detects learning trouble of some sort, such as confusion about the problem they are working on or a participant who is dominating the discussion or not interacting with the other participants. In order to recognize problems in the dialogue, we investigated conversational elements that can be utilized as predictors for effective and ineffective interaction between human students. These elements can serve as the basis for student and group models. In this paper, we discuss group interaction during collaborative learning, our representation of participant dialogue, and the statistical models we are using to determine the role being played by a participant at any point in the dialogue and the effectiveness of the group. We also describe student and group models that can be built using conversational elements and discuss one set that we built to illustrate their potential value in collaborative learning. An erratum to this article is available at .     

一种基于强化学习的学习Agent

强化学习通过感知环境状态和从环境中获得不确定奖赏值来学习动态系统的最优行为策略,是构造智能Agent的核心技术之一,在面向Agent的开发环境AODE中扩充BDI模型,引入策略和能力心智成分,采用强化学习技术实现策略构造函数,从而提出一种基于强化学习技术的学习Agent,研究AODE中自适应Agent物结构和运行方式,使智能Agent具有动态环境的在线学习能力,有效期能够有效地满足Agent各种心智要求。     

基于多Agent的高校教学评测系统模型研究

基于多Agent技术所构造的协同设计系统具有多Agent系统的分布性、协作性和智能性的特点。在分析目前高校教师教学评价系统的基础上，提出了一个基于多Agent系统（MAS）的高校教师教学评测系统模型（MATES），并着重讨论了该系统中各Agent的功能和Agent之间的协作关系。该模型为教师教学评价的自动化、智能化和高效率提供了一个良好的计算平台。     

Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents

Dynamic area coverage with small unmanned aerial vehicle (UAV) systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process. Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved. In this paper, we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems. The proposed decentralized decision-making dynamic area coverage (DDMDAC) method utilizes reinforcement learning (RL) where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment. Intelligent agents increase their global observations by gathering information about the environment by connecting with other agents. The connectivity provides a consensus for the decision-making process, while each agent takes decisions. At each step, agents acquire all reachable agents’ states, determine the optimum location for maximal area coverage and receive reward using the covered rate on the target area, respectively. The method was tested in a multi-agent actor-critic simulation platform. In the study, it has been considered that each UAV has a certain communication distance as in real applications. The results show that UAVs with limited communication distance can act jointly in the target area and can successfully cover the area without guidance from the central command unit.     

基于多Agent的个性化协作学习系统研究与实现

远程教育技术的高速发展为远程学习者提供了极大的便利,使得学生可以在任何时候任何地点学习适合自己的内容。然而远程学习者地理上的分散性,也不可避免地产生了大量的孤独学习者。因此怎样提供一种有效的方法,将具有相同兴趣的学生组织到一起,并帮助他们在学习过程中能够共同分享学习经验、交流学习资料,已逐渐成为革新E—Learning技术中的一个研究热点。该文构建了一个基于多Agent机制的个性化协作学习系统,并提出了一种新颖的打分／交换的用户动态聚类算法,从学生的资源请求中发现学生兴趣,并有效地将具有相同兴趣的学生自动组成学习社区。实验证明,该算法具有较高的效率和良好的可扩展性。     

基于多Agent的混合智能学习算法及

本文提出了基于多Agent的混合智能学习算法，将个体学习和群体学习有效地结合起来，并给出了该算法在RoboCup足球机器人仿真系统中的具体应用．实验结果表明了算法的可行性与有效性．     

基于角色和CSCL的智能网络协作模型

为深入研究智能Agent在开放动态的网络环境中的应用，把角色机制应用到网络学习环境中，提出了一种新型的基于CSCL的智能网络协作模型，给出了智能Agent的结构表示及功能，并从多角色的角度给出了模型中Agent的分类。最后以共同学习活动为例，对Agent之间基于角色的协作过程进行了形式化描述。     

A recommender agent based on learning styles for better virtual collaborative learning experiences

Almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Based on a survey related to how students find additional bibliographical resources for university courses, we concluded there is a strong need for recommended learning materials, for specialized online search and for personalized learning tools. As a result, we developed an educational collaborative filtering recommender agent, with an integrated learning style finder. The agent produces two types of recommendations: suggestions and shortcuts for learning materials and learning tools, helping the learner to better navigate through educational resources. Shortcuts are created taking into account only the user’s profile, while suggestions are created using the choices made by the learners with similar learning styles. The learning style finder assigns to each user a profile model, taking into account an index of learning styles, as well as patterns discovered in the virtual behavior of the user. The current study presents the agent itself, as well as its integration to a virtual collaborative learning environment and its success and limitations, based on users’ feedback.     

Collaborative virtual learning environments: design and evaluation

E-learning systems have gone through a radical change from the initial text-based environments to more stimulating multimedia systems. Such systems are Collaborative Virtual Environments, which could be used in order to support collaborative e-learning scenarios. The main aim of this paper is to aid educational designers in selecting, designing and evaluating three dimensional collaborative virtual environments in order to gain the pedagogical benefits of Computer Supported Collaborative Learning. Therefore, this paper initially discusses the potential of three dimensional networked virtual environments for supporting collaborative learning. Furthermore, based on a two-step platform selection process this paper (a) presents and compares three dimensional multi-user virtual environments for supporting collaborative learning and (b) validates the most promising solution against a set of design principles for educational virtual environments. According to these principles, an educational environment has been implemented on top of the selected platform in order to support collaborative e-learning scenarios. The design of this environment is also presented. In addition, this paper presents the results of three small scale studies carried out in a tertiary education department, to assess the educational environment. This environment has been evaluated based on a hybrid evaluation methodology for uncovering usability problems, collecting further requirements for additional functionality to support collaborative virtual learning environments, and determining the appropriateness of different kinds of learning scenarios.

协同学习环境中的嵌套式知识空间模型和感知处理

为使协同学习环境能提供合理的学习导航和灵活的感知处理,提出了嵌套式知识空间模型,其中包含知识域、知识域之间的4种关系和知识空间等．根据该模型建立的协同学习环境,可以对系统中的文档资料进行有效的组织和管理,帮助学习者找到学习目标以及所需的资料进行学习．为了消除用户在远程协同学习环境中的“孤独感”,在嵌套式知识空间模型的基础上,提出了协同学习环境中的多级感知处理模型．它包括两种感知方式：多层感知空间方式和定制监测方式．此外在感知处理模型中,为用户提供了信息过滤器和隐私保护器,可有效地进行信息过滤和隐私保护．