Causal Bayesian Networks (CBNs) are an important tool for reasoning under uncertainty in complex real-world systems. Determining the graphical structure of a CBN remains a key challenge and is undertaken either by eliciting it from humans, using machine learning to learn it from data, or using a combination of these two approaches. In the latter case, human knowledge is generally provided to the algorithm before it starts, but here we investigate a novel approach where the structure learning algorithm itself dynamically identifies and requests knowledge for relationships that the algorithm identifies as uncertain during structure learning. We integrate this approach into the Tabu structure learning algorithm and show that it offers considerable gains in structural accuracy, which are generally larger than those offered by existing approaches for integrating knowledge. We suggest that a variant which requests only arc orientation information may be particularly useful where the practitioner has little preexisting knowledge of the causal relationships. As well as offering improved accuracy, the approach can use human expertise more effectively and contributes to making the structure learning process more transparent.

翻译：因果贝叶斯网络（CBN）是复杂现实系统中不确定性推理的重要工具。确定CBN的图形结构仍是一项关键挑战，通常通过人类专家引导、从数据中利用机器学习自动学习或两者结合的方式实现。在后一种方法中，人类知识通常在算法运行前被提供，但本文探索了一种新颖方法：结构学习算法本身在结构学习过程中动态识别并请求与不确定关系相关的知识。我们将此方法集成至禁忌搜索结构学习算法中，并证明其在结构准确性方面带来了显著提升，这种提升通常大于现有知识集成方法的效果。我们提出一种仅请求弧方向信息的变体，该变体在实践者对因果关系缺乏先验知识时尤为实用。该方法不仅提高了准确性，还能更高效地利用人类专业知识，并有助于提升结构学习过程的透明度。