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首页> 外文期刊>International journal of computer science and network security >Learning Model Transformation Rules from Examples: The GAILP System
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Learning Model Transformation Rules from Examples: The GAILP System

机译：从示例中学习模型转换规则：GAILP系统

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摘要

Learning by examples refers to acquiring knowledge and experience to generalize theory from existing examples. Inductive logic programming (ILP) uses inductive inference to generate hypotheses from examples given with a background knowledge. ILP systems have been successfully applied in a number of real-world domains. Several ILP systems were introduced in the literature. Each system uses different search strategies and heuristics however, most systems employed a single predicate learning approach, which is not applicable in many learning problems. In this paper, we present GAILP, an ILP system that overcomes this limitation. GAILP employs genetic algorithms to discover various aspects of combinations to induce a set of hypotheses. It appraises such combinations in different ways to extract the most generic ones. The paper presents a thorough evaluation of the foundational aspects of the learning capability of GAILP. Two experiments were conducted to learn software model transformation rules. Experimental results reveal that GAILP is superior to a prominent ILP system, namely ALEPH, in different aspects and specifically in learning multi-predicates incrementally. We used a case study of tasks from the automated software engineering domain. The results obtained for the “class packaging” task showed that the accuracy of GAILP was 0.88 comparing with 0.83 achieved by ALEPH. Similarly, for “introducing Fa?ade interface” task, the accuracy obtained using GAILP and ALEPH were 0.90 and 0.66 respectively.
机译：通过实例学习是指获取知识和经验以从现有实例中推广理论。归纳逻辑编程（ILP）使用归纳推理从具有背景知识的示例中生成假设。 ILP系统已成功应用于许多实际领域。文献中介绍了几种ILP系统。每个系统使用不同的搜索策略和试探法，但是，大多数系统采用单个谓词学习方法，这不适用于许多学习问题。在本文中，我们介绍了GAILP，它是一种克服了此限制的ILP系统。 GAILP使用遗传算法来发现组合的各个方面，以得出一组假设。它以不同的方式评估此类组合，以提取最通用的组合。本文全面评估了GAILP学习能力的基础方面。进行了两个实验以学习软件模型转换规则。实验结果表明，GAILP在各个方面，特别是在逐步学习多个谓词方面，均优于著名的ILP系统ALEPH。我们使用了来自自动化软件工程领域的任务的案例研究。针对“类包装”任务获得的结果表明，GAILP的精度为0.88，而ALEPH的精度为0.83。同样，对于“引入Fafaade接口”任务，使用GAILP和ALEPH获得的精度分别为0.90和0.66。

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