广义理由模型的不一致先例论证解释框架
An Argumentative Explanation Framework for Generalized Reason Model with Inconsistent Precedents
AI总结：
本文针对广义理由模型在不一致先例下的推理，提出扩展推导状态论证框架以提供论证解释，填补了该领域论证解释方法的空白。
摘要：
先例约束是人工智能与法律领域中基于案例推理的基础之一。它通常假设底层先例集合必须是一致的。为放宽这一假设，引入了理由模型的广义概念。尽管已有多种基于传统一致理由模型的先例推理论证解释方法，但对于这种容纳不一致先例的广义推理框架，尚未开发出相应的论证解释方法。为解决这一问题，本文考察了推导状态论证框架（DSA框架）的一种扩展，以根据理由模型的广义概念来解释推理。
英文摘要：
Precedential constraint is one foundation of case-based reasoning in AI and Law. It generally assumes that the underlying set of precedents must be consistent. To relax this assumption, a generalized notion of the reason model has been introduced. While several argumentative explanation approaches exist for reasoning with precedents based on the traditional consistent reason model, there has been no corresponding argumentative explanation method developed for this generalized reasoning framework accommodating inconsistent precedents. To address this question, this paper examines an extension of the derivation state argumentation framework (DSA-framework) to explain the reasoning according to the generalized notion of the reason model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
