2007 Fiscal Year Final Research Report Summary
A study on agent based evaluation methods to reduce damage at urban area
| Project/Area Number |
17500099
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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 | Meijo University |
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Principal Investigator |
TAKAHASHI Tomoichi Meijo University, Faculty of Science and Technology, Department of Information Engineering, Professor (80278259)
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| Project Period (FY) |
2005 – 2007
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| Keywords | agent / damage reduction / simulation engineer / social simu |
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| Research Abstract |
When disasters threaten human life, we want to use agent-based social simulation (ABSS) to predict the evacuation behavior of person and to estimate damage of disasters. When the ABSS is applied to practical usages, it is necessary to demonstrate the validity of the ABSS's outputs to the potential users. In scientific and engineering fields, the process Guess-Compute consequence-Compare experiment results with simulation results has been repeatedly used to increase the fidelity of simulations. Social phenomena are in contrast to the phenomena that can be explained using the laws of natural science.
The Social phenomena are not so much objectively measured but subjectively interpreted by humans. It makes difficult to systematically analyze the social phenomena and to evaluate the performances of agents. In most cases, it is difficult to obtain data on real cases or conduct experiments to verify the results of ABSS and the analysis results.
In this research, considering the disaster and rescue simulation system as an example of ABSS, methods to analyze the simulation outputs without any metrics related with the application domain have been studied and the interpretation of the analysis results are shown. The probability model has been widely used to model the variations in the parameters of simulation and agent characters. A large number of simulations with changing them statistically are used to verify simulation results. For example, Johnson, et al. have simulated the evacuation of public buildings and proposed risk assessment techniques. Our approach uses a stochastic approach to represent the states of the agents and analyze their behaviors.
The analysis method is based on mathematical quantity such as rank, eigenvalue, etc. They are independent of domain specific evaluations and suggest the potential of the method that will be applied to other fields.
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Research Products
(18 results)
