We present a computational argumentation approach that models legal reasoning with evidence and proof as dialectical rather than probabilistic. This hybrid approach of stories and arguments models the process of proof in a way that is compatible with Allen and Pardo's theory of relative plausibility by adding arguments that can be used to show how evidence can support or attack explanations. Using some legal cases as examples, we show how criteria for assessing explanations connect arguments and evidence to story schemes. We show how this hybrid dialectical approach avoids the main problem of the probabilistic approaches, namely that they require precise numbers to be applied in order to decide legal cases. We provide an alternative method that allows fact-finders to reason with evidence holistically and not in the item-by-item fashion proposed by the probabilistic account.

中文翻译：

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采取辩证立场进行论证论证

我们提出了一种计算论证方法，该方法将证据和证明的法律推理建模为辩证的而非概率的。这种故事和论证的混合方法通过添加可用于显示证据如何支持或攻击解释的论证，以与艾伦和帕尔多的相对合理性理论兼容的方式对证明过程进行建模。以一些法律案例为例，我们展示了评估解释的标准如何将论点和证据与故事方案联系起来。我们展示了这种混合辩证方法如何避免概率方法的主要问题，即它们需要应用精确的数字来决定法律案件。