用对话式论证解释知识库中的逻辑推理，让矛盾处理更清晰易懂。
Dialogue-based Explanations for Logical Reasoning using Structured Argumentation
- 基于论证框架构建对话式推理机制，支持不一致容忍的逻辑推理。
- 通过对话证明树形式化解释查询结果，比传统方法更直观、表达力更强。
- 适合需要透明推理过程的AI系统，如智能问答与决策支持场景。
知识库中不一致容忍推理的解释问题是人工智能领域的重要课题。现有方法生成的解释常缺乏关键信息，或无法有效表达非二元冲突。本文分析了当前最先进方法的结构缺陷，提出一种通用的论证驱动方法，适用于涉及最大一致子集的逻辑体系，并展示了如何将此类逻辑转化为论证形式。该方法提供对话模型作为辩证证明过程，用于计算和解释查询答案，对应于不一致容忍语义下的结论。由此可构建辩证证明树作为解释，相较于现有形式更富表达性且更具直观性。
原文摘要 · Abstract (English)
The problem of explaining inconsistency-tolerant reasoning in knowledge bases (KBs) is a prominent topic in Artificial Intelligence (AI). While there is some work on this problem, the explanations provided by existing approaches often lack critical information or fail to be expressive enough for non-binary conflicts. In this paper, we identify structural weaknesses of the state-of-the-art and propose a generic argumentation-based approach to address these problems. This approach is defined for logics involving reasoning with maximal consistent subsets and shows how any such logic can be translated to argumentation. Our work provides dialogue models as dialectic-proof procedures to compute and explain a query answer wrt inconsistency-tolerant semantics. This allows us to construct dialectical proof trees as explanations, which are more expressive and arguably more intuitive than existing explanation formalisms.
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