| Project/Area Number |
07455165
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
System engineering
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| Research Institution | Osaka University |
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Principal Investigator |
KOMODA Norihisa Osaka University, Faculty of Engineering, Professor, 工学部, 教授 (90234898)
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| Co-Investigator(Kenkyū-buntansha) |
IKKAI Yoshitomo Osaka University, Faculty of Engineering, Research Assistant, 工学部, 助手 (40273578)
OHKAWA Takenao Osaka University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (30223738)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 1996: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1995: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | Planning Problem / Expert System / Knowledge Engineering / Inductive Learning / Knowledge Acquisition / 帰納学習 |
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| Research Abstract |
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.
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