Abstract Argumentation for Case-Based Reasoning

被引：0
|
作者：
Cyras, Kristijonas [1 ]
Satoh, Ken [2 ]
Toni, Francesca [1 ]
机构：
[1] Imperial Coll London, London, England
[2] Natl Inst Informat, Tokyo, Japan
来源：
FIFTEENTH INTERNATIONAL CONFERENCE ON THE PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING | 2016年
关键词：
EXPLANATION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We investigate case-based reasoning (CBR) problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. To this end, we employ abstract argumentation (AA) and propose a novel methodology for CBR, called AA-CBR. The argumentative formulation naturally allows to characterise the computation of an outcome as a dialogical process between a proponent and an opponent, and can also be used to extract explanations for why an outcome for a new case is (not) computed.
引用
收藏
页码：549 / 552
页数：4
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