In human-robot interaction policy design, a rule-based method is efficient,
explainable, expressive and intuitive. In this paper, we present the
Signal-Rule-Slot framework, which refines prior work on rule-based symbol
system design and introduces a new, Bayesian notion of interaction rule utility
called Causal Pathway Self-information. We offer a rigorous theoretical
foundation as well as a rich open-source reference implementation Ravestate,
with which we conduct user studies in text-, speech-, and vision-based
scenarios. The experiments show robust contextual behaviour of our
probabilistically informed rule-based system, paving the way for more effective
human-machine interaction.

在人机交互政策设计中，一种基于规则的方法是高效、可解释、表达力强且直观的。本文介绍了 Signal-Rule-Slot 框架，该框架改进了基于规则的符号系统设计的先前工作，并引入了一种新的贝叶斯交互规则效用概念 - 因果路径自信息。我们提供了严格的理论基础，以及一个丰富的开源参考实现 Ravestate，通过该实现我们在基于文本、语音和视觉的场景中进行了用户研究。实验证明了我们基于概率信息的基于规则的系统具有强大的上下文行为，为更有效的人机交互铺平了道路。

Ravestate：基于因果特异性引导的交互策略的分布式组合

Ravestate: Distributed Composition of a Causal-Specificity-Guided  Interaction Policy

Multiple-choice questions with item-writing flaws can negatively impact
student learning and skew analytics. These flaws are often present in
student-generated questions, making it difficult to assess their quality and
suitability for classroom usage. Existing methods for evaluating
multiple-choice questions often focus on machine readability metrics, without
considering their intended use within course materials and their pedagogical
implications. In this study, we compared the performance of a rule-based method
we developed to a machine-learning based method utilizing GPT-4 for the task of
automatically assessing multiple-choice questions based on 19 common
item-writing flaws. By analyzing 200 student-generated questions from four
different subject areas, we found that the rule-based method correctly detected
91% of the flaws identified by human annotators, as compared to 79% by GPT-4.
We demonstrated the effectiveness of the two methods in identifying common
item-writing flaws present in the student-generated questions across different
subject areas. The rule-based method can accurately and efficiently evaluate
multiple-choice questions from multiple domains, outperforming GPT-4 and going
beyond existing metrics that do not account for the educational use of such
questions. Finally, we discuss the potential for using these automated methods
to improve the quality of questions based on the identified flaws.

本研究采用基于规则和基于 GPT-4 的基于机器学习方法评估了 200 个学生生成的多项选择题目中 19 个常见选项撰写错误。结果显示基于规则的方法可以更准确，更高效地评估多个学科领域的选择题，并在教育使用方面具有潜在校准的作用。

使用 GPT-4 和基于规则的方法评估多项选择题的质量

Assessing the Quality of Multiple-Choice Questions Using GPT-4 and  Rule-Based Methods

Background. In recent years, libraries and archives led important
digitisation campaigns that opened the access to vast collections of historical
documents. While such documents are often available as XML ALTO documents, they
lack information about their logical structure. In this paper, we address the
problem of Logical Layout Analysis applied to historical documents in French.
We propose a rule-based method, that we evaluate and compare with two
Machine-Learning models, namely RIPPER and Gradient Boosting. Our data set
contains French newspapers, periodicals and magazines, published in the first
half of the twentieth century in the Franche-Comté Region. Results. Our
rule-based system outperforms the two other models in nearly all evaluations.
It has especially better Recall results, indicating that our system covers more
types of every logical label than the other two models. When comparing RIPPER
with Gradient Boosting, we can observe that Gradient Boosting has better
Precision scores but RIPPER has better Recall scores. Conclusions. The
evaluation shows that our system outperforms the two Machine Learning models,
and provides significantly higher Recall. It also confirms that our system can
be used to produce annotated data sets that are large enough to envisage
Machine Learning or Deep Learning approaches for the task of Logical Layout
Analysis. Combining rules and Machine Learning models into hybrid systems could
potentially provide even better performances. Furthermore, as the layout in
historical documents evolves rapidly, one possible solution to overcome this
problem would be to apply Rule Learning algorithms to bootstrap rule sets
adapted to different publication periods.

本文探讨了应用于法国历史文件的逻辑版面分析问题，并提出了一种基于规则的方法，并对两种机器学习模型 RIPPER 和 Gradient Boosting 进行了评估和比较。通过实验表明，我们的规则系统表现优于两种机器学习模型，提高了召回率，并证实了我们的系统可以生成足够大的注释数据集，以便于机器学习或深度学习方法进行逻辑版面分析任务。