Developing reasoning capabilities for intelligent agents that facilitate adaptive learning

开发智能代理的推理能力，促进自适应学习

• 批准号: 262147-2008
• 负责人: Lin, Fuhua
• 金额: $ 1.09万
• 依托单位: Athabasca University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=503185
• 关键词: Developing; reasoning; capabilities; intelligent; agents

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.

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，并通过提供针对个体学习者的属性的补救或辅导，使其行为适应学习者的需求。为了适应其行为，人工智能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。在一个AL系统与基于代理的架构，智能代理与每个学习者或一个特定的任务，以模拟课程讲师或导师执行教学任务。基于Agent的人工智能系统由于其分布性和任务复杂性，被构造为多Agent系统。智能体在开放环境中合理适应的能力取决于拥有事实知识，如课程知识、学习者知识、教育资源知识，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领域、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、协调一组任务特定代理的干预、学习者的能力、学习者的能力和学习者的能力。以及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线推理机制，并且推理机制的领域特定性质限制了系统本身的可重用性。该提案强调了一种形式化人工智能系统代理推理模型和机制的新方法。在短期内，本研究将专注于设计算法的自适应课程规划，自适应测试，自适应联盟形成协作学习，并应用不同的模型和机制，以不同的任务，以确定哪一个选项是最合适的，在每一个实例。从长远来看，本研究将尝试开发一个完全开放和可扩展的，基于代理的学习环境。研究中开发的技术将促进现代分布式学习的进一步研发。此外，通过该项目培训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。