为不一致判例下的推理提供可解释的论证框架
An Argumentative Explanation Framework for Generalized Reason Model with Inconsistent Precedents
- 扩展论证框架，处理判例不一致情况下的推理过程
- 构建可解释机制，揭示非一致性前提下的逻辑推导
- 适合法律AI中需要解释复杂推理的研究者
判例约束是人工智能与法律领域案例推理的基础，通常假设判例集合必须一致。为放宽这一假设，引入了广义理由模型的概念。尽管针对传统一致理由模型已有多种论证式解释方法，但尚无对应方法用于解释容纳不一致判例的广义推理框架。本文探讨将推导状态论证框架（DSA-framework）扩展，以解释基于广义理由模型的推理过程。
原文摘要 · Abstract (English)
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.
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