【作者】 李誌；

【导师】 余雪丽；

【作者基本信息】 太原理工大学 ， 计算机应用技术， 2012， 博士

【摘要】 学习支持系统是近年来人工智能在教育中应用的研究热点之一,它是教育学、认知科学和计算机科学的交叉研究领域。多Agent系统的理论与技术,为分布式开放系统的分析、设计和实现提供了一条崭新的途径,因此也被广泛应用于学习支持系统。随着计算机与网络的发展,人们日渐依赖于通过网络进行交互,人们的学习方式也发生了变化,这就要求学习支持系统相应改变,从而对实现它的技术有了更高要求。本文从分析新形势下人类教育理念的转变和新学习方式入手,对多Agent强化学习系统展开研究,包括单Agent的强化学习算法和多Agent强化学习算法,并对实际应用所需要的用户个性化描述、个性化学习内容呈现等问题进行了探索,主要完成了以下几项研究工作：(1)以历史发展为线索总结了计算机辅助教学的智能化历程,分析了智能教学系统的优势与不足,认为适应性学习支持系统是当前数字化学习支持平台的发展趋势。(2)在对经典强化学习算法TD算法和Q学习算法进行深入研究的基础上,提出了一种基于偏向信息的强化学习算法,利用预置的偏向信息或先验知识来指导Agent行为选择策略,引导Agent探测状态空间的方向,同时在学习的过程中,不断修改、完善已有的知识,达到提高算法收敛速度的目的。(3)提出了适合于连续状态空间下的多Agent分层强化学习的半马氏博弈模型SMG,同时给出了此模型对应的MAHRL协同框架,分别对协作子任务和非协作子任务进行形式化描述,阐述了多Agent分层强化学习系统的工作流程并给出了MAHRL(?)(?)同框架的核心-基于Pareto(?)与优解的分层强化学习算法。仿真实验验证了文中所提到的SMG模型、MAHRL(?)办同框架和基于Pareto占优解的分层强化学习算法的有效性和优越性。(4)根据卡特尔16项个性因素测验法,提出通过心理测验获得受训者量化的关键个性属性值的算法。(5)提出了恐怖场景个性化呈现算法,结合本文提出的强化学习算法实现了技能学习,可根据受训者的知识技能掌握情况调整操作难度和知识测试点,既防止难度过大使受训者迷茫而失去信心,又避免枯燥重复的操作和测试使其厌烦而失去兴趣。最后,本文以煤矿救援培训为实例实现了基于视觉听觉语义相干性的训练系统原型,该系统可以根据用户的个性化特征信息,获取与之相适应的素材,组合在一起提供给用户,实现了个性化培训。更多还原

【Abstract】 Adaptive learning support system has been the focus topic in the research field of artificial intelligence in education in recent years, which is a cross areas of education, cognitive science and computer science. The theory and techniques in Multi-agent system can be used as a novel method to analyze, design and implement distributed open system, so it is also applied in learning support system. With the rapid development of computer and network technology, people rely more and more on network in communication. This causes the change of learning style. The result is that the learning support system has to be changed and require higher demands on its implemental techniques.In this paper, we start our research on Multi-agent reinforcement learning system, includes the reinforcement learning algorithms of Agent and Multi-agent, from analyzing the change of learning style. The paper also focuses on the critical application technologies such as user profile, personalized learning environment. The paper has completed the following tasks: (1) We summarize the being intelligent process of computer-aided instruction based on literature reviews and analyze the strengths and weaknesses of intelligent tutoring system, and hold that adaptive learning support system is the current trend of the e-learning platform.(2)The biasing reinforcement learning algorithm is presented based on detailed analysis of TD algorithm and Q-learning algorithm. The bias information is incorporated to boost learning process with priori knowledge to affect the action selection strategies in reinforcement learning. The error in priori knowledge has been modified during the learning process and the learning speed is also accelerated.(3) The Semi-Markov Game Model is presented which can express the hierarchical learning tasks of Multi-Agent system effectively and temporal and sequence characteristic of joint action. This kind of model can be used to Multi-Agent hierarchical reinforcement learning on the continuous state space. Then the paper gives the collaborative framework of MAHRL based on SMG model. This framework describes the collaborative and non-collaborative tasks among agents respectively, and elaborates the work flow of MAHRL system. Finally, the paper gives the HRL algorithm based on Pareto optimal solutions. And this algorithm is the kernel of the collaborative framework of MAHRL. The experiment testifies the validity and superiority of those kinds of model, framework and algorithm.(4) An algorithm is presented based on the sixteen personality factor questionnaire to obtain the key personality value of trainee.(5) Personalized rendering algorithm for terrorist scene is also presented. Combining with the reinforcement learning algorithm presented in this paper, it can be used to adjust the difficulty level of the practices.Finally, a prototype of system on the mine accident rescue training is realized. The system can obtains the user’s personality data, retrieve the matching knowledge according the related rules, and then provide personalized learning environment to users.更多还原