ABSTRACT Analogical reasoning is a complex process based on a comparison between two pairs of concepts or states of affairs (aka. the source and the target) for characterizing certain features from one to another. Arguments which employ this process to support their claims are called analogical arguments. Our goals are to study the structure and the computation for their defeasibility in light of the argumentation theory. Our proposed assumption-based argumentation with predicate similarity ABA(p) framework can be seen as an extension of assumption-based argumentation framework (ABA), in which not only assumptions can be used but also similarity of predicates is used to support a claim. ABA (p) labels each argument tree with an analogical degree and different ways to aggregate numerical values are studied toward gullible/skeptical characteristics in agent reasoning. The acceptability of analogical arguments is evaluated w.r.t. the semantics of abstract argumentation. Finally, we demonstrate that ABA (p) captures the argumentation scheme for argument from analogy and provides an explanation when it is used for persuasion.

中文翻译：

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论说服推理类比论证的构建与评价

摘要 类比推理是一个复杂的过程，它基于两对概念或事件状态（也就是源和目标）之间的比较，以从一个到另一个表征某些特征。使用此过程来支持其主张的论证称为类比论证。我们的目标是根据论证理论研究其可废止性的结构和计算。我们提出的具有谓词相似性的基于假设的论证 ABA(p) 框架可以看作是基于假设的论证框架 (ABA) 的扩展，其中不仅可以使用假设，而且还可以使用谓词的相似性来支持声明。ABA (p) 用类比度标记每个参数树，并且针对智能体推理中的易受骗/怀疑特征研究了聚合数值的不同方法。类比论证的可接受性是根据抽象论证的语义来评估的。最后，我们证明 ABA (p) 从类比中捕捉论证的论证方案，并在用于说服时提供解释。