Repast HPCとGPUを用いた大規模エージェント シミュレーションの実装
書誌事項
- タイトル別名
-
- Implementation of Massive Agent Model Using Repast HPC and GPU
抄録
<p>Multi-Agent Simulation (MAS) is efficient for analysis of various social mechanisms. Recently, there are many studies on massive agent model to explain more complex social phenomena. Then, we aim for implementation of large scale simulation model using Repast HPC toolkit, a platform for massive agent model. In this article, we build "Schelling Segregation Model" for spatial model using geospatial data provided OpenStreetMap, an open source project creating a free editable map. In this model, agents are located continuous space , not grid in original. When an agent is "unhappy" and migrate to new location, it costs agents some simulation time depending on distance between old location and new one. This article reports simulation results using Japanese cities and verification result about execution time.</p>
収録刊行物
-
- 人工知能学会第二種研究会資料
-
人工知能学会第二種研究会資料 2013 (DOCMAS-005), 03-, 2013-10-22
一般社団法人 人工知能学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390852174975126016
-
- NII論文ID
- 130008079757
-
- ISSN
- 24365556
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用可