A New Stochastic Local Search Approach for Computing Preferred Extensions of Abstract Argumentation

被引：2
|
作者：
Niu, Dangdang [1 ]
Liu, Lei [1 ]
Lu, Shuai [1 ,2 ,3 ]
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
[2] Jilin Univ, Coll Math, Changchun 130012, Peoples R China
[3] Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R China
来源：
ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2016年 / 285卷
关键词：
D O I：
10.3233/978-1-61499-672-9-1652
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we proposed a new stochastic local search algorithm Inc-CCAEP for computing the preferred extensions in (abstract) argumentation frameworks (AF). Inc-CCAEP realizes an incremental version of Swcca, specially designed for computing the preferred extensions in AF. Experiments show that, Inc-CCAEP notably outperforms the state-of-the-art solvers consistently on random benchmarks with nonempty preferred extensions.
引用
收藏
页码：1652 / 1653
页数：2
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