| Project/Area Number |
06452161
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
機械工作・生産工学
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| Research Institution | Kobe University |
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Principal Investigator |
UEDA Kanji Department of Mechanical Engineering, Faculty of Engineering, Kobe University, Professor, 工学部, 教授 (50031133)
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| Co-Investigator(Kenkyū-buntansha) |
OHKURA Kazuhiro Department of Mechanical Engineering, Faculty of Engineering, Kobe University, R, 工学部, 助手 (40252788)
MANABE Keiji Department of Mechanical Engineering, Faculty of Engineering, Kobe University, R, 工学部, 助手 (90209677)
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| Project Period (FY) |
1994 – 1995
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| Project Status |
Completed (Fiscal Year 1995)
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| Budget Amount *help |
¥7,000,000 (Direct Cost: ¥7,000,000)
Fiscal Year 1995: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1994: ¥6,200,000 (Direct Cost: ¥6,200,000)
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| Keywords | Biological Manufacturing Systems / Pseudo Ecosystem / Artificial Life / Emergence / Genetic Algorithms / Reinforcement Learning / Manufacturing Systems / Artifactual Engineering |
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| Research Abstract |
Conceptual Development of Biological-Oriented Manufacturing System was purposed as a pseudo ecosystem of artifacts. Especially, the multi-developmental process from original materials to products in detail was discussed, from the point of view of the explanation of protein synthesis in molecular biology.
Evolution and Learning in a Biological-Oriented Manufacturing System
The formulation of assembly models required for autonomous assembly and its notation method is developed, named the graph of Connection Precedence Relationship (CPR-graph).
Evolution and Learning in a Biological-Oriented Manufacturing System
A mathematical model of a simple Biological-Oriented Manufacturing System was purposed. The manufacturing system was considered as a pseudo ecosystem, in which the different kinds of agents and the production requests were species and the global environment, respectively. An extended genetic algorithm was developed, named GA with neutral mutations, motivated by the phenomenon of the directed evolution in bacteria. The optimization performance was examined by solving problems including GA-difficulty, such as nonstationarity and deception.
In addition, two learning schema required was developed for the manufacturing system. One of them was the composite stochastic learning automata in order to obtain the cooperative behavior among the group of agents, the other was a new efficient reinforcement learning algorithm in a dynamic environment.
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