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摘要翻译：
在E-learning中，仍然存在着如何确保学习者在学习过程中的个性化和持续跟踪的问题，实际上，在提出的众多工具中，很少有系统专注于学习者的实时跟踪。我们在这一领域的工作发展了一个基于动态案例推理的多智能体系统的设计和实现，该系统能够启动学习并为学习者提供个性化的跟踪。在与平台交互时，每个学习者都在机器中留下了他/她的痕迹。这些痕迹以情景的形式储存在一个基础上，这些情景丰富了集体过去的经验。该系统监视、比较和分析这些轨迹，以保持一个恒定的智能手表，并因此检测阻碍进展的困难和/或避免可能的辍学。该系统可以支持任何学习科目。一个基于案例的推理系统的成功关键取决于所使用的检索步骤的性能，更具体地说，取决于用于检索与学习者过程相似的场景（正在进行的痕迹）的相似性度量。我们提出了一种互补相似性度量，称为反最长公共子序列(ILCSS)。为了帮助和指导学习者，系统配备了虚拟和人类相结合的导师。
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英文标题：
《Multi-Agents Dynamic Case Based Reasoning and The Inverse Longest Common
Sub-Sequence And Individualized Follow-up of Learners in The CEHL》
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作者：
Abdelhamid Zouhair, El Mokhtar En-Naimi, Benaissa Amami, Hadhoum
Boukachour, Patrick Person, Cyrille Bertelle
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
In E-learning, there is still the problem of knowing how to ensure an individualized and continuous learner's follow-up during learning process, indeed among the numerous tools proposed, very few systems concentrate on a real time learner's follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. When interacting with the platform, every learner leaves his/her traces in the machine. These traces are stored in a basis under the form of scenarios which enrich collective past experience. The system monitors, compares and analyses these traces to keep a constant intelligent watch and therefore detect difficulties hindering progress and/or avoid possible dropping out. The system can support any learning subject. The success of a case-based reasoning system depends critically on the performance of the retrieval step used and, more specifically, on similarity measure used to retrieve scenarios that are similar to the course of the learner (traces in progress). We propose a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). To help and guide the learner, the system is equipped with combined virtual and human tutors.
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PDF链接：
https://arxiv.org/pdf/1209.6395
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