用带不确定性的逻辑门压缩智能辅导系统的参数量，提升实时反馈速度。
Intelligent tutoring systems by Bayesian nets with noisy gates
- 用带不确定性的逻辑门构建贝叶斯网络的条件概率表，大幅减少参数数量
- 推导专用推理算法，在保持精度前提下显著加快计算速度
- 适合需要快速响应的智能辅导系统开发人员参考
有向图模型如贝叶斯网络常用于实现能够实时与学习者交互的智能辅导系统。在使用此类模型时，控制参数数量至关重要：一方面，模型通常基于专家知识构建，过多参数会阻碍实践者采用；另一方面，参数数量影响推理复杂度，而实时反馈要求快速查询计算。本文主张在辅导系统所用的贝叶斯网络中，采用带不确定性的逻辑门来实现条件概率表的紧凑参数化。文中讨论了需采集的模型参数语义及应用该方法所需假设，并推导出一种专用推理方案以加速计算。
原文摘要 · Abstract (English)
Directed graphical models such as Bayesian nets are often used to implement intelligent tutoring systems able to interact in real-time with learners in a purely automatic way. When coping with such models, keeping a bound on the number of parameters might be important for multiple reasons. First, as these models are typically based on expert knowledge, a huge number of parameters to elicit might discourage practitioners from adopting them. Moreover, the number of model parameters affects the complexity of the inferences, while a fast computation of the queries is needed for real-time feedback. We advocate logical gates with uncertainty for a compact parametrization of the conditional probability tables in the underlying Bayesian net used by tutoring systems. We discuss the semantics of the model parameters to elicit and the assumptions required to apply such approach in this domain. We also derive a dedicated inference scheme to speed up computations.
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