Co-evolutionary Multi-agent Learning by means of Genetic Programming
Project/Area Number |
11480071
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Research Category |
Grant-in-Aid for Scientific Research (B).
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Allocation Type | Single-year Grants |
Section | 一般 |
Research Field |
Intelligent informatics
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Research Institution | UNIVERSITY OF TOKYO |
Principal Investigator |
IBA Hitoshi University of Tokyo, School of Frontier Science, Associate Professor, 大学院・新領域創成科学研究科, 助教授 (40302773)
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Co-Investigator(Kenkyū-buntansha) |
ISHIZUKA Mitsuru University of Tokyo, School of Engineering, Professor, 大学院・工学系科学研究科, 教授 (50114369)
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Project Period (FY) |
1999 – 2000
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Project Status |
Completed (Fiscal Year 2000)
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Budget Amount *help |
¥7,200,000 (Direct Cost: ¥7,200,000)
Fiscal Year 2000: ¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 1999: ¥3,900,000 (Direct Cost: ¥3,900,000)
|
Keywords | Evolutionary System / Multi-agent System / Genetic Algorithms / Genetic Programming / Machine Learning / Co-evolution / Symbiosis / マルチエージェント |
Research Abstract |
It is not easy to describe in advance the program which controls more than one agent effectively. This is due to the difficulty with multi-agent programming for the communication, cooperation, synchronization and conflict resolution. Thus, we propose a multi-agent learning scheme based on the co-evolutionary method for the purpose of the automatic generation of the programs for many agents. Our approach is based upon the evolutionary computation, i.e., genetic programming and genetic algorithms. The established multi-agent learning system has been applied to several practical problems, and the validity has been shown. The application areas include the wider field of examples as described below : (1) Adaptive WWW search by the Web robot. (2) Automatic synthesis of analog circuits, such as an asymmetric bandpass filter. (3) Inference of gene regulatory networks, i.e., Weaver models or differential equation systems, for genome informatics. (4) Interactive music composition. (5) Evolutionary quantum circuit design, such as quantum teleportation and oracle problems. (6) Financial data prediction, such as Nikke225 or foreign exchange rate.
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Report
(3 results)
Research Products
(18 results)
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[Publications] Iba, H.: "Evolving Multiple Agents by Genetic Programming"Genetic Programming 3, Spector, L., Langdon, W.B., O'Reilly, U.-A., and Angeline, P.A., (eds.), MIT Press. 447-466 (1999)
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[Publications] Nikolaev, N.I., Iba, H., Slavov, V.: "Inductive Genetic Programming with Immune Network Dynamics"Genetic Programming 3, Spector, L., Langdon, W.B., O'Reilly, U.-A., and Angeline, P.A., (eds.), MIT Press. 355-376 (1999)
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[Publications] Ito, T., Iba, H., Sato, S.: "A Self-Tuning Mechanism for Depth-Dependent Crossover"Genetic Programming 3, Spector, L., Langdon, W.B., O'Reilly, U.-A., and Angeline, P.A., (eds.), MIT Press. 377-399 (1999)
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[Publications] Iwata, M., Kajitani, I., Murakawa, M., Hirao, Y., Iba, H., Higuchi, T.: "Pattern Recognition System using Evolvable Hardware"Systems and Computers in Japan. vol.31, no.4. 1-11 (2000)
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