Developing reasoning capabilities for intelligent agents that facilitate adaptive learning

自适应学习系统(AL系统)能够通过个性化的教育课程和内容，并通过提供针对个别学习者的特性而定制的补习或辅导，来使其行为适应学习者的需要。为了适应其行为，AL系统必须考虑学习者在学习目标、知识、经验、学习风格和学习偏好方面的差异。在具有基于代理的体系结构的AL系统中，智能代理与每个学习者或特定任务相关联，以模拟课程教师或执行教学任务的导师。由于其分布性和任务的复杂性，基于代理的人工智能系统被构造为多代理系统。代理在开放环境中理性适应的能力取决于拥有事实知识，如课程、学习者、教育资源的知识，最重要的是拥有推理能力。推理能力包括“理解”学习者的特征和知识域；计划和优化学习计划和学习活动的能力；更新学习模型的能力；协调一组特定于任务的代理的干预；以及用于协作学习的联盟形成等。然而，现有的学习系统具有针对特定领域的硬连接的推理机制，并且推理机制的特定领域的性质限制了系统本身的可重用性。这一建议突出了一种新的方法，用于形式化AL系统的代理的推理模型和机制。在短期内，本研究将集中于为协作学习设计自适应课程规划、自适应测试和自适应联盟形成的算法，并将不同的模型和机制应用于不同的任务，以确定在每种情况下哪个选项最合适。从长远来看，本研究将试图开发一个完全开放和可扩展的、基于代理的学习环境。研究中将开发的技术将促进现代分布式学习的进一步研究和开发。此外，通过该项目培养的学生将成为基于代理的智能系统的先驱工程师或研究人员。

An adaptive learning system (AL system) is able to adapt its behavior to the learner's needs by personalizing educational curricula and contents, and by providing remedial or tutorial tailored to the properties of the individual learner. To adapt its behavior, an AL system must consider the differences among learners in terms of learning objectives, knowledge, experience, learning styles, and learning preferences. In an AL system with an agent-based architecture, an intelligent agent is associated with each learner or a specific task to simulate a course instructor or tutor performing pedagogical tasks. Due to its distributed nature and task complexity, an agent-based AL system is structured as a multi-agent system. The abilities of the agents to adapt rationally in an open environment hinge on possessing factual knowledge, such as knowledge of curricula, learners, educational resources, and, most importantly, possessing reasoning capabilities. The reasoning capabilities include "understanding" learner characteristics and knowledge domains; the ability to plan and optimize study plans and learning activities; the ability to update learning models; coordination of the intervention of a set of task-specific agents; and coalition formation for collaborative learning, etc. Existing learning systems, however, have their reasoning mechanisms hardwired for specific domains, and the domain-specific nature of the reasoning mechanisms restricts the reusability of the systems themselves. This proposal highlights a new methodology for formalizing the reasoning models and mechanisms for the agents of AL systems. In the short-term, this research will focus on designing algorithms for adaptive course planning, adaptive testing, and adaptive coalition formation for collaborative learning, and applying different models and mechanisms to different tasks to determine which option is the most appropriate in each instance. In the long-term, this research will attempt to develop a fully open and scalable, agent-based learning environment. The technology to be developed in the research will facilitate further R&D in modern distributed learning. Also, the students to be trained through this project will become pioneer engineers or researchers in agent-based intelligent systems.