.png){kind=icon}
返回
Constraint acquisition
.png){kind=icon}
OA
AI
DOI：10.1016/j.artint.2015.08.001.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user in the modeling task. This paper presents the basic architecture for acquiring constraint networks from examples classified by the user. The theoretical questions raised by constraint acquisition are stated and their complexity is given. We then propose CONACQ, a system that uses a concise representation of the learner's version space into a clausal formula. Based on this logical representation, our architecture uses strategies for eliciting constraint networks in both the passive acquisition context, where the learner is only provided a pool of examples, and the active acquisition context, where the learner is allowed to ask membership queries to the user. The computational properties of our strategies are analyzed and their practical effectiveness is experimentally evaluated. (C) 2015 Elsevier By. All rights reserved.
Keyword:
Constraint programming
Constraint learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.9
论文数:
6.1K
被引数:
1.9W
机构
引用论文
分享收藏Study on Electromagnetic–Dynamic Coupled Modeling Method—Detection by Stator Current of the Induction Motors with Bearing Faults.png)
Machines
OAAI
MachinesIF0
OAAI分享收藏分享收藏Spectrochemical Analysis Using Low-Background Laser Plasma Induced by Nd-YAG Laser at Low Pressure
PREAI
分享收藏
查看更多内容