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A Generic Planning Method with Knowledge Acquisition Function

一种具有知识获取功能的通用规划方法

基本信息

批准号：
07455165
负责人：
KOMODA Norihisa
金额：
$ 1.6万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1996
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-07455165/
关键词：
Planning Problem Expert System Knowledge Engineering Inductive Learning Knowledge Acquisition 帰納学習

项目摘要

We devised several kinds of automatic learning method for planning knowledge which are based on Status Selection Planning Method that we had proposed. They enable to constract a planning systems which can deal with generic planning problems. ID3, which is one of typical inductive learning methods, was applied to planning knowledge acquisition, and format of training data, which enables to learn planning knowledge was proposed. Because the inductive learning have a problem of how to gather a great deal of training data, recursive type training data gathering method was proposed. In the method, knowledge is divided into several stages of knowledge, gathering training data and application of inductive learning are recursively repreated one by one from the latest stage knowledge, in which it is easy to gathering training data. These methods were applied to flow shop problems and job shop problems. From the experimentation, it was confirmed that these methods could generate the superior knowledge than human knowledge. The automatic learning methods for planning knowledge decreases time for planning, so that it is obvious that these method is effective for CIM,in which short time planning for a large scale problem is necessary.As mention above, by introducing automatic learning feature of planning knowledge, we actualized a generic planning architecture which supported planning comprehensively.

在提出的状态选择规划方法的基础上，设计了几种规划知识的自动学习方法。它们使我们能够建立一个能够处理一般规划问题的规划系统。将典型的归纳学习方法ID3应用于规划知识的获取，提出了能够学习规划知识的训练数据格式。针对归纳学习中存在的大量训练数据收集问题，提出了递归式训练数据收集方法。该方法将知识划分为多个知识阶段，从最后一个阶段的知识开始，依次递归地进行训练数据的采集和归纳学习的应用，使得训练数据的采集变得容易。这些方法被应用到流水车间问题和作业车间问题。实验结果表明，这些方法可以产生比人类知识上级的知识。规划知识的自动学习方法减少了规划的时间，因此这些方法对于需要在短时间内规划大规模问题的CIM是有效的。如上所述，通过引入规划知识的自动学习特性，我们实现了一个全面支持规划的通用规划体系结构。