Extracting Constraints for Process Modeling

被引：0

作者：

Bridewell, Will ^{[1
]}

Borrett, Stuart R. ^{[1
]}

Todorovski, Ljupco ^{[1
]}

机构：

[1] Stanford Univ, Computat Learning Lab, CSLI, Stanford, CA 94305 USA

来源：

K-CAP'07: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE | 2007年

关键词：

declarative bias; inductive process modeling;

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：

In this paper, we introduce an approach for extracting constraints on process model construction. We begin by clarifying the type of knowledge produced by our method and how one may apply it. Next, we review the task of inductive process modeling, which provides the required data. We then introduce a logical formalism and a computational method for acquiring scientific knowledge from candidate process models. Results suggest that the learned constraints make sense ecologically and may provide insight into the nature of the modeled domain. We conclude the paper by discussing related and future work.

引用

页码：87 / 94

页数：8

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