Prediction of Resident Evacuation during Flooding Using Multi-Agent Simulation with Reinforcement Learning
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- YOSHIDA Tsukasa
- Nippon Telegraph and Telephone (NTT)
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- OKUMURA Kazuki
- Nippon Telegraph and Telephone (NTT)
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- ARAKAWA Yutaka
- Nippon Telegraph and Telephone (NTT)
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- NOMOTO Narichika
- Nippon Telegraph and Telephone (NTT)
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- IIDA Yasuhiro
- Nippon Telegraph and Telephone (NTT)
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- NAKAMURA Takao
- Nippon Telegraph and Telephone (NTT)
Bibliographic Information
- Other Title
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- 強化学習を用いたマルチエージェントシミュレーションによる水害時の住民避難予測
Abstract
<p>It is important to improve simulation performance since floods that cause enormous damage have been increasing in recent years. In previous studies of multi-agent simulation, the destination of the agents was generally fixed and their actions were restricted. As a result, it was not possible to reproduce the movements that humans could take in the event of a real disaster, resulting in a discrepancy with actual human behavior. To solve this problem, we propose a new paradigm for evacuation forecasting which regarded human behavior as a resource recovery game, and to learn it by reinforcement learning. Additionally, we propose reinforcement learning using Monte Carlo Tree Search (MCTS) as a method for solving this game. In the evaluation experiments, the MCTS algorithm was able to learn the correct behavior, and reproduced the situation where some people stayed in the flooded area like the actual behavior.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2023 (0), 4Xin156-4Xin156, 2023
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390578283198279424
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- ISSN
- 27587347
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed