A Research of machine learning of object oriented analysis knowledge by induvtive reasoning

归纳推理面向对象分析知识的机器学习研究

基本信息

批准号：
09680377
负责人：
HARADA Minoru
金额：
$ 1.92万
依托单位：
Aoyama Gakuin University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1999
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09680377/
关键词：
theory revision discrimination rule progol prolog object oriented analysis automation rule base maintenance 帰納学習 progol 帰納推論格フレーム生成オブジェクト指向分析自然語処理

项目摘要

A lot of rule-based systems are presently in use. However, the reliabilities of these systems depend primarily on the rules initially given by the expert on the domain. No expert is capable of preparing a perfect rule base because creating a complete set of rules would require that every possible situation has been considered, Hence, if an incorrect execution result comes out, it is usual to correct the rule base by manual operation. The purpose of the present study is to develop system THERES (Theory Revision System) which automates this correction of rules. The rule base which THERES revises is a Horn clause set described with prolog. The predicates which compose the rule base are assumed to be divided into the correct predicates (called basic predicates) which need not be revised and the predicates (called rule predicates) as the target of maintenance, whose body section is described by basic predicates (single rule decidable rule base). After the rule base system is applied to a ne … More w problem, only by dividing the results into the positive cases and the negative cases and also by pointing out the new positive cases which were not derived by the rule base but are thought to be correct, THERES automatically revises the rule base so as to solve this problem correctly. THERES first searches incorrect rule R, then acquires discrimination rule D derived from R using the inductive learning system Progol to separate positive and negative examples of R, and finally adds the body of D to that of R in the form of a conjunction. The proposed system corrects incorrect rules as if adding missing regulations on exceptions to the existing rules created by an expert. Therefore, it is more practical than to replace the existing rules by the rules acquired by inductive learning from the cases. The present study applies THERES to revise the rule base of an automatic object-oriented analysis system CAMEO, a practical rule base system previously developed by us for automating object oriented analysis, in order to discuss the effectiveness of the proposed method. Less

使用许多基于规则的系统。但是，这些系统的可靠性主要取决于专家在域上最初给出的规则。没有专家能够准备完美的规则基础，因为创建完整的规则将要求考虑到所有可能的情况，因此，如果出现不正确的执行结果，通常是通过手动操作来纠正规则库。本研究的目的是开发系统（理论修订系统），该系统可以自动化这种规则的纠正。修订的规则基础是用序言描述的角条款集。假定构成规则基库的谓词被认为分为不需要修改的正确谓词（称为基本谓词），而谓词（称为规则谓词）是维护的目标，其身体部分由基本谓词（单个规则可决定的规则基础）描述。在规则基本系统应用于NE…更多问题之后，仅通过将结果划分为正案件和负面案例，并指出了规则基础并未得出的新阳性案例，但认为这是正确的，它会自动逆转规则基础，以便正确解决此问题。首先搜索错误的规则R，然后使用电感学习系统Progol从R中获取歧视规则D，以分离R的正面和负面示例，并最终以连接的形式将D的正体添加到R的主体中。提议的系统纠正了错误的规则，就好像在专家创建的现有规则中添加了关于例外的遗失法规一样。因此，这比通过从案件中归纳学习获得的规则来代替现有规则更实用。本研究适用于修改自动面向对象的分析系统客串的规则基础，这是我们以前开发的用于自动化对象的分析的实用规则基础系统，以讨论所提出方法的有效性。较少的

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

原田実、野村佳秀、山本幸二、大野雅志、田村浩樹、高橋史郎: "自然語要求仕様からオブジェクト指向設計図を自動生成するシステムCAMEO"情報処理学会論文誌. 38. 2031-2039 (1997)

Minoru Harada、Yoshihide Nomura、Koji Yamamoto、Masashi Ohno、Hiroki Tamura、Shiro Takahashi：“CAMEO，一种根据自然语言需求规范自动生成面向对象蓝图的系统”日本信息处理学会杂志 38. 2031-2039。 (1997)