用文字和图形增强贝叶斯网络推理解释，提升用户理解效果
Comparing verbal, visual and combined explanations for Bayesian Network inferences
- 设计文字、图形及二者结合的界面扩展，引导用户理解推理路径
- 联合使用文字与图形，对观察影响路径等问题的解答准确率更高
- 适合需要解释复杂推理过程的非专业用户或教育场景
贝叶斯网络（BNs）是辅助概率推理的重要工具，尽管被视为透明模型，但人们仍难以理解。当前用户界面（UI）未能有效阐明贝叶斯网络的推理过程。为此，我们设计了文字和图形两种扩展界面，帮助用户理解常见推理模式。通过用户研究对比了文字、图形及联合界面扩展与基准界面的效果。主要发现：（1）三种扩展方式均显著优于基准界面，尤其在理解观测影响、影响路径以及多观测相互作用方面；（2）文字与图形联合使用，在部分问题类型上表现优于单一模态。
原文摘要 · Abstract (English)
Bayesian Networks (BNs) are an important tool for assisting probabilistic reasoning, but despite being considered transparent models, people have trouble understanding them. Further, current User Interfaces (UIs) still do not clarify the reasoning of BNs. To address this problem, we have designed verbal and visual extensions to the standard BN UI, which can guide users through common inference patterns. We conducted a user study to compare our verbal, visual and combined UI extensions, and a baseline UI. Our main findings are: (1) users did better with all three types of extensions than with the baseline UI for questions about the impact of an observation, the paths that enable this impact, and the way in which an observation influences the impact of other observations; and (2) using verbal and visual modalities together is better than using either modality alone for some of these question types.
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