A Probabilistic Semantics for Abstract Argumentation

被引:106
|
作者
Thimm, Matthias [1 ]
机构
[1] Univ Koblenz Landau, Inst Web Sci & Technol, Landau, Germany
来源
20TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2012) | 2012年 / 242卷
关键词
LOGIC;
D O I
10.3233/978-1-61499-098-7-750
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Classical semantics for abstract argumentation frameworks are usually defined in terms of extensions or, more recently, labelings. That is, an argument is either regarded as accepted with respect to a labeling or not. In order to reason with a specific semantics one takes either a credulous or skeptical approach, i.e. an argument is ultimately accepted, if it is accepted in one or all labelings, respectively. In this paper, we propose a more general approach for a semantics that allows for a more fine-grained differentiation between those two extreme views on reasoning. In particular, we propose a probabilistic semantics for abstract argumentation that assigns probabilities or degrees of belief to individual arguments. We show that our semantics generalizes the classical notions of semantics and we point out interesting relationships between concepts from argumentation and probabilistic reasoning. We illustrate the usefulness of our semantics on an example from the medical domain.
引用
收藏
页码:750 / 755
页数:6
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