| Project/Area Number |
07650305
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent mechanics/Mechanical systems
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
SAWARAGI Tetsuo Kyoto University, Graduate School of Engineering, Associate Professor, 工学研究科, 助教授 (10187304)
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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 |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1996: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 1995: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | decision support / genetic algorithms / knowledge acquisition / interface agent / concept formation / influence diagram / schema theory / knowledge dicovery / 意思決定論 / インフルエンスダイアグラム / し尿処理プラント / 概念クラスタリング / ガス製造プラント |
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| Research Abstract |
In an operation of a large-scaled and complex plant, it is usual that the expertise needed for the operation is distributed among a number of human operators and they have to collaborate with each other sharing and/or exchanging the individual decisions as well as their background concepts on the current status of the plant.
In the first year of this research project, we attemp to explicate the expert operator's cognitive map concerning with this plant by influence diagrams and developed a method for deriving a set of control rules that is rationally designed for establishing the coordinate control of multiple processes. Finally, the derived control rules were applied to an actual plant for sewage disposal and the control result was shown. Then, we attempted to design an interface system that has a capabilities of forming the hierarchically organized concepts on the anomalous behaviors of plants just as the human operators have when they make judgments on the plant status from the incom
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ing stream of measuremnt data. We adopted a concept formation method that is one of the inductive learning methods of artificial intelligence, and design a human-friendly interface system that can support a man-machine collaborative work environment.
In the second year, based on what is attained in the previous year we presented a method for concept formation for an interface agent that affempts to capture user's internal conceptual structure by observing interactions between an user and a system. Our proposing algorithm consisted of two phases : the one for selecting the essential attributes out of a provided set of attributes that may initially contain both relevant and irrelevant ones, and the other for constructing new attributes using genetic algorithms applied to a set of elementary features logically represented in a disjunctive normal form. Our method was applied to an artificial data set and its practical usage for apprentice learning was discussed. Then, we formulated an interface agent's making high-stakes, time-critical decisions. The formulation was made with respect to the hierarchically organized concepts of plant anomalies that are availble to an interface agent by applying a concept formation method. Less
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