1996 Fiscal Year Final Research Report Summary
SELF-ORGANIZING MULTI-AGENT SYSTEMS : ARTIFICIAL LIFE APPROACHES
| Project/Area Number |
07680402
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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 informatics
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| Research Institution | THE UNIVERSITY OF TOKUSHIMA |
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Principal Investigator |
ONO Norihiko THE UNIVERSITY OF TOKUSHIMA,FACULTY OF ENGINEERING,ASSOCIATE PROFESSOR, 工学部, 助教授 (60194594)
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| Project Period (FY) |
1995 – 1996
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| Keywords | MULTI-AGENT SYSTEMS / ARTIFICIAL LIFE / REINFORCEMENT-LEARNING / MACHINE LEARNING / ARTIFICIAL LIFE / DISTRIBUTED ARTIFICIAL INTELLIGENCE / GENETIC ALGORITHMS |
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| Research Abstract |
In attempting to let artificial organisms or simple reactive robots synthesize some coordinated behavior, several researchers in the fields of artificial life and robotics have applied monolithic reinforcement-learning algorithms to multi-agent learning problems. In most of these applications, only a small number of learning agents are engaged in their joint tasks and accordingly the state space for each agent is relatively small. This is the reason why monolithic reinforcement-learning algorithms have been successfully applied to these multi-agent learning problems.
However, these straightforward applications of reinforcement-learning algorithms do not successfully scale up to more complex multi-agent learning problems, where not a few learning agents are engaged in some coordinated tasks. In such a multi-agent problem domain, agents should appropriately behave according to not only sensory information produced by the physical environment itself but also that produced by other agents, and hence the state space for each reinforcement-learning agent grows exponentially in the number of agents operating in the same environment.
Even simple multi-agent learning problems are computationally intractable by the monolithic reinfocrement-learning approaches. We consider a modified version of the Pursuit Problem as such a multi-agent learning problem, and show how successfully modular Q-learning prey-pursuing agents synthesize coordinated decision policies needed to capture a randomly-moving prey agent.
Multi-agent learning is an extremely difficult problem in general, and the results we obtained strongly-rely on specific attributes of the problem. But the results are quite encouraging and suggest that our modular reinforcement-learning approach is promising in studying adaptive behavior of multiple autonomous agents.
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