Boosting Constraint Acquisition via Generalization Queries
Boosting Constraint Acquisition via Generalization Queries
复制标题
通过泛化查询促进约束获取
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
E. Bouyakhf
中科院分区：
文献类型：
--
作者：
C. Bessiere;Rémi Coletta;Abderrazak Daoudi;Nadjib Lazaar;Younes Mechqrane;E. Bouyakhf
约束获取帮助非专家用户将其问题建模为约束网络。在现有的约束获取系统中，用户只被要求回答非常基本的问题。缺点是，当没有提供背景知识时，用户可能需要回答大量这样的问题来学习所有约束。本文引入了基于变量类型聚集的泛化查询的概念。提出了一种可以嵌入到任何约束获取系统中的约束泛化算法。我们提出了几种策略，以使我们的方法在查询数量方面更有效。最后，我们通过实验将最近的QUACQ系统与通过使用我们的泛化功能而增强的扩展版本进行了比较。结果表明，扩展后的QUACQ显著提高了基本QUACQ。
Constraint acquisition assists a non-expert user in modeling her problem as a constraint network. In existing constraint acquisition systems the user is only asked to answer very basic questions. The drawback is that when no background knowledge is provided, the user may need to answer a great number of such questions to learn all the constraints. In this paper, we introduce the concept of generalization query based on an aggregation of variables into types. We present a constraint generalization algorithm that can be plugged into any constraint acquisition system. We propose several strategies to make our approach more efficient in terms of number of queries. Finally we experimentally compare the recent QUACQ system to an extended version boosted by the use of our generalization functionality. The results show that the extended version dramatically improves the basic QUACQ.