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

页码：549 / 552

页数：4

共 19 条

[1]

[Anonymous], 2013, CASE BASED REASONING

[2]

Athakravi D., 2015, LPNMR

[3]

Bench-Capon T, 2009, P 12 INT C ART INT L, P118, DOI DOI 10.1145/1568234.1568248

[4] Constructing argument graphs with deductive arguments: a tutorial [J].