首页> 外文会议>Fifteenth International Florida Artificial Intelligence Research Society Conference, May 14-16, 2002, Pensacola Beach, Florida >Modeling and Implementing Intelligent Educational Environments Using an Interdisciplinary Approach
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Modeling and Implementing Intelligent Educational Environments Using an Interdisciplinary Approach

机译:使用跨学科方法对智能教育环境进行建模和实施

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This paper presents the results that we have been achieving with our research involving educational environments based on Intelligent Tutoring Systems (ITS) architecture using a MAS (Multi-Agent System approach. A general guideline was idealized to be use as a reference for our research group to design and to implement intelligent educational software. Specially, interactive educational software modeled as a game. We believe that now we have the agent's technology that makes possible to build interesting solving problems environments. However, it is very important to remember that educational process is closely connected with country culture. Educational environments that work very well in a specific context will not necessarily perform in the same way in a different place. It can be viewed as a restriction in many ways: as applicability for other educational reality, pedagogical paradigm, and so on. However, we argue that educational process has a "kernel" of necessities and aspects to be observed. When you intend to work considering learner as central person in the educational process, under the "learn-to-learn" paradigm. So, it causes an immediately reflection in the way we design and model an educational software. We discuss our ideas and guidelines used to model and to implement our systems under pedagogical viewpoint. We explore the possibilities of designing such systems using a MAS (Muti-agent) approach in order to explore the possibilities of this technique. In our proposal the domain is modeled with reactive agents and some of them we applied techniques of Machine Learning (Reinforcement Learning). The students and the set of pedagogical agents (task agents, assistants, and tutors) were modeled as cognitive agents using BDI architecture (belief, desire, and intention). Due to the space we are not describing the systems in details, but exemplifying how the guidelines were used.
机译:本文介绍了我们的研究成果,该研究涉及使用MAS(多智能体系统方法)的基于智能辅导系统(ITS)架构的教育环境,一般准则已被理想化,可作为我们研究组的参考设计和实现智能教育软件,特别是以游戏为模型的交互式教育软件,我们相信现在有了代理人的技术可以构建有趣的解决问题的环境,但是,记住教育过程非常重要与乡村文化密切相关的内容。在特定情况下运作良好的教育环境不一定会在不同的地方以相同的方式发挥作用。在许多方面,它可以被视为一种限制:对于其他教育现实的适用性,教学范式,但是,我们认为教育过程具有必要性和方面的“内核”。观察。当您打算在学习过程中以学习者为中心时,以学习者为中心。因此,它立即引起我们设计和建模教育软件的方式的反思。我们讨论在教学论观点下用于建模和实施系统的思想和指导方针。我们探索使用MAS(Muti-agent)方法设计此类系统的可能性,以探索该技术的可能性。在我们的建议中,使用反应性代理对领域进行建模,其中一些我们应用了机器学习(强化学习)技术。使用BDI架构(信念,愿望和意图)将学生和教学代理(任务代理,助手和辅导员)的集合建模为认知代理。由于篇幅所限,我们没有详细描述系统,而是举例说明如何使用准则。

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