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A voting-based agent system to support personalised e-learning in a course selection scenario

机译:基于投票的代理系统,用于在课程选择场景中支持个性化电子学习

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摘要

Agent technologies are a promising approach to solving a number of prob-lems concerned with personalised learning due to the inherent autonomy and independence they provide for learners. The objective of this thesis is to find out whether a multiagent system could potentially replace a centralised infra-structure, and to explore the impact of agents taking different strategies. More specifically, our aim is to show how intelligent agent systems can not only form a good framework for distributed e-learning systems, but also how they can be applied in contexts where learners are autonomous and independent. The study also aims to investigate fairness issues and propose a simple framework of fair-ness definitions derived from the relevant literature. To this end, a university course selection scenario has been chosen, where the university has many courses available, but has only sufficient resources to run the most preferred ones. Instead of a centralised system, we consider a de-centralised approach where individuals can make a collective decision about which courses should run by using a multi-agent system based on voting. This voting process consists of multiple rounds, allowing a student agent to accurate-ly represent the student’s preferences, and learn from previous rounds. The ef-fectiveness of this research is demonstrated in three experiments. The first ex-periment explores whether voting procedures and multiagent technology could potentially replace a centralised infrastructure. It also explores the impact of agents using different strategies on overall student satisfaction. The second ex-periment demonstrates the potential for using multiagent systems and voting in settings where students have more complex preferences. The last experiment investigates how intelligent agent-based e-learning systems can ensure fairness between individuals using different strategies. This work shows that agent technology could provide levels of decentrali-sation and personalisation that could be extended to various types of personal and informal learning. It also highlights the importance of the issue of fairness in intelligent and personalised e-learning systems. In this context, it may be said that there is only one potential view of fairness that is practical for these systems, which is the social welfare view that looks to the overall outcome.
机译:由于代理技术为学习者提供了固有的自主权和独立性,因此它是解决与个性化学习有关的许多问题的有前途的方法。本文的目的是发现多主体系统是否有可能取代集中式基础设施,并探讨采取不同策略的主体的影响。更具体地说,我们的目标是展示智能代理系统如何不仅可以为分布式电子学习系统形成一个良好的框架,而且还可以将其应用于学习者自主且独立的环境中。该研究还旨在调查公平问题,并提出一个从相关文献中得出的公平定义的简单框架。为此,选择了大学课程选择方案,其中大学有许多可用课程,但只有足够的资源来运行最喜欢的课程。代替集中式系统,我们考虑一种去中心化的方法,在该方法中,个人可以通过使用基于投票的多代理系统来集体决定应开设哪些课程。该投票过程包括多个回合,使学生代理人可以准确地代表学生的喜好,并可以从以前的回合中学习。通过三个实验证明了这项研究的有效性。第一个实验探讨了投票程序和多代理技术是否有可能取代集中式基础架构。它还探讨了代理人使用不同策略对整体学生满意度的影响。第二期实验展示了在学生偏好更为复杂的环境中使用多代理系统和投票的潜力。上一个实验研究了基于智能主体的电子学习系统如何确保使用不同策略的个人之间的公平。这项工作表明,代理技术可以提供去中心化和个性化的水平,并且可以扩展到各种类型的个人和非正式学习中。它还强调了智能和个性化电子学习系统中公平问题的重要性。在这种情况下,可以说,对于这些系统而言,只有一种潜在的公平观点是可行的,即从整体上看待社会福利的观点。

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    Aseere Ali;

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  • 年度 2012
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