Tractable POMDP representations for intelligent tutoring systems
Tractable POMDP representations for intelligent tutoring systems
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DOI：
10.1145/2438653.2438664
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发表时间：
2013-03
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影响因子：
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通讯作者：
J. Folsom-Kovarik;G. Sukthankar;S. Schatz
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文献类型：
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作者：
J. Folsom-Kovarik;G. Sukthankar;S. Schatz
借助部分可观察马尔可夫决策过程 (POMDP)，智能辅导系统 (ITS) 可以根据有限的证据对个体学习者进行建模，并在存在不确定性的情况下提前计划。然而，POMDP 需要适当的表示才能在 ITS 中变得易于处理，ITS 可以模拟许多学习者特征，例如个人技能的掌握或特定误解的存在。本文介绍了两种 POMDP 表示形式（状态队列和观察链），它们利用 ITS 任务属性并让 POMDP 扩展以表示 100 多个独立的学习器特征。以现实世界的军事训练问题为例。人体研究（n = 14）为模型构建提供了初步验证。最后，通过模拟学生评估实验表征有助于预测其对 ITS 性能的影响。压缩表示可以对各种模拟问题进行建模，其教学效果与无损表示相同。通过提高可处理性，POMDP ITS 可以容纳更多或更详细的学习者状态和输入。
With Partially Observable Markov Decision Processes (POMDPs), Intelligent Tutoring Systems (ITSs) can model individual learners from limited evidence and plan ahead despite uncertainty. However, POMDPs need appropriate representations to become tractable in ITSs that model many learner features, such as mastery of individual skills or the presence of specific misconceptions. This article describes two POMDP representations—state queues and observation chains—that take advantage of ITS task properties and let POMDPs scale to represent over 100 independent learner features. A real-world military training problem is given as one example. A human study (n = 14) provides initial validation for the model construction. Finally, evaluating the experimental representations with simulated students helps predict their impact on ITS performance. The compressed representations can model a wide range of simulated problems with instructional efficacy equal to lossless representations. With improved tractability, POMDP ITSs can accommodate more numerous or more detailed learner states and inputs.