当前位置: X-MOL 学术Knowl. Eng. Rev. › 论文详情
Predictive models and abstract argumentation: the case of high-complexity semantics
The Knowledge Engineering Review ( IF 1.257 ) Pub Date : 2019-04-18 , DOI: 10.1017/s0269888918000036
Mauro Vallati; Federico Cerutti; Massimiliano Giacomin

In this paper, we describe how predictive models can be positively exploited in abstract argumentation. In particular, we present two main sets of results. On one side, we show that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argumentation framework. On the other side, we show that predictive models predict significant aspects of the solution to the preferred extensions enumeration problem. By exploiting an extensive set of argumentation framework features—that is, values that summarize a potentially important property of a framework—the proposed approach is able to provide an accurate prediction about which algorithm would be faster on a given problem instance, as well as of the structure of the solution, where the complete knowledge of such structure would require a computationally hard problem to be solved. Improving the ability of existing argumentation-based systems to support human sense-making and decision processes is just one of the possible exploitations of such knowledge obtained in an inexpensive way.
更新日期:2020-03-20

 

全部期刊列表>>
物理学研究前沿热点精选期刊推荐
chemistry
自然职位线上招聘会
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
ACS ES&T Engineering
ACS ES&T Water
ACS Publications填问卷
屿渡论文,编辑服务
阿拉丁试剂right
南昌大学
王辉
南方科技大学
彭小水
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
赵延川
李霄羽
廖矿标
朱守非
试剂库存
down
wechat
bug