2004 Fiscal Year Final Research Report Summary
Learning and Evolution of Intelligent Systems Composed of Multi-individuals Interacting with Each Other
| Project/Area Number |
14350212
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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 | Waseda University |
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Principal Investigator |
HIRASAWA Kotaro Waseda University, Graduate School of Information, Production, and Systems, Professor, 大学院・情報生産システム研究科, 教授 (70253474)
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| Co-Investigator(Kenkyū-buntansha) |
FURUZUKI Takayuki (HU Jinglu) Waseda University, Graduate School of Information, Production, and Systems, Associate Professor, 大学院・情報生産システム研究科, 助教授 (50294905)
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| Project Period (FY) |
2002 – 2004
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| Keywords | learning / evolution / symbiosis / reinforcement learning / Genetic Algorithm / intelligent systems / interaction / multi-agent |
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| Research Abstract |
Intelligent system consisting of multi-individuals interacting between each other have been developed and the learning and evolution of the system have been studied. It is shown that the proposed system has better performance than conventional methods.
1.Intelligent agents : Genetic Network Programming (GNP) has been developed as intelligent agents, which represents solutions using directed graph structures, and showed better performance than the conventional method in creating agent behavior.
2.Interaction between agents : In order to create intelligent interaction between agents composed of GNP, Multi-Agent Systems with Symbiotic Learning and Evolution (Masbiole) has been developed, which applied symbiotic relations in the ecosystem such as Mutualism, Predation, Competition and Altruism. From the results of the simulations using Tileworld problem, it is clarified that the proposed method shows complex interaction between agents by introducing the symbiotic strategies.
3.Learning and Evolution in Multi-agent systems : Generally, living things has been developing through evolution and learning. Evolution has been done for a long period of time, and learning is done based on trial-and-error during lifetime of each individual. Based on this idea, a GNP algorithm has been developed by -using evolution and reinforcement learning. This method can automatically create programs which show better performance than the GNP based on evolution only and the conventional evolutionary computation (Genetic Programming) because the proposed method can improve the programs during task execution (online learning) in addition to the evolution executed after task execution.
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Research Products
(18 results)
