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首页> 外文会议>Sustaining TEL: From innovation to learning and practice >Ultra-Personalization and Decentralization: The Potential of Multi-Agent Systems in Personal and Informal Learning
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Ultra-Personalization and Decentralization: The Potential of Multi-Agent Systems in Personal and Informal Learning

机译：超个人化和去中心化：个人和非正式学习中多智能体系统的潜力

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Agents are autonomous software components that work with one another in a decentralized fashion to achieve some end. Agent systems have been used in Technology Enhanced Learning (TEL) before, but these applications seldom take advantage of the fact that each agent may have its own goals and strategies, which makes agent systems an attractive way of providing personalized learning. In particular, since agents can solve problems in a decentralized way, this makes them an attractive way of supporting informal learning. In this paper we use scenarios to examine how common problem solving techniques from the agents world (voting, coalition formation and auction systems) map to significant challenges for personalized and informal learning in the TEL world. Through an agent simulation we then show how an agent system might perform in one of those scenarios and explore how different agent strategies might influence the outcome. Based on this work we argue that agent systems provide a way of providing ultra-personalization of the learning process in a decentralized way and highlight equita-bility and scrutability as two key challenges for future investigation.
机译：代理是自治软件组件，它们以分散的方式相互协作以达到某种目的。代理系统以前曾在技术增强学习（TEL）中使用，但是这些应用很少利用每个代理都有自己的目标和策略这一事实，这使得代理系统成为提供个性化学习的一种有吸引力的方式。特别是，由于代理可以以分散的方式解决问题，因此这使他们成为支持非正式学习的有吸引力的方式。在本文中，我们使用场景来研究代理商世界（投票，联盟形成和拍卖系统）中常见的问题解决技术如何映射到TEL世界中个性化和非正式学习的重大挑战。通过代理仿真，我们然后展示了代理系统在这些场景之一中的性能，并探讨了不同的代理策略如何影响结果。基于这项工作，我们认为代理系统提供了一种以分散的方式提供学习过程的超个性化的方法，并强调了均衡性和可写性是未来研究的两个主要挑战。

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