论文标题
使用概率的认知论证中的法律解释自动推理
Explainable Automated Reasoning in Law using Probabilistic Epistemic Argumentation
论文作者
论文摘要
在法律上应用自动推理工具进行决策支持和分析有可能使法院的决策更加透明和客观。由于证据的准确性和相关性通常存在不确定性，因此需要非古典推理方法。在这里，我们调查了概率认知论证，作为有关法律案件的自动推理的工具。我们介绍了一项一般计划，将法律案件模拟为概率的认知论证问题，解释如何对证据进行建模，并勾勒出如何自动产生法律决策的解释。我们的框架很容易解释，可以处理循环结构并不精确的概率，并保证在最坏情况下多项式概率推理。
Applying automated reasoning tools for decision support and analysis in law has the potential to make court decisions more transparent and objective. Since there is often uncertainty about the accuracy and relevance of evidence, non-classical reasoning approaches are required. Here, we investigate probabilistic epistemic argumentation as a tool for automated reasoning about legal cases. We introduce a general scheme to model legal cases as probabilistic epistemic argumentation problems, explain how evidence can be modeled and sketch how explanations for legal decisions can be generated automatically. Our framework is easily interpretable, can deal with cyclic structures and imprecise probabilities and guarantees polynomial-time probabilistic reasoning in the worst-case.
