| Project/Area Number |
20500125
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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 | University of Tsukuba |
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Principal Investigator |
KURAHASHI Setsuya University of Tsukuba, 大学院・ビジネス科学研究科, 准教授 (40431663)
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| Co-Investigator(Kenkyū-buntansha) |
TSUDA Kazuhiko 筑波大学, 大学院・ビジネス科学研究科, 教授 (50302378)
YOSHIDA Kenichi 筑波大学, 大学院・ビジネス科学研究科, 教授 (40344858)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2010: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2009: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2008: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | シミュレーション / エージェント / 遺伝的アルゴリズム / 社会ネットワーク / モデル選択 / パターン / 温暖化ガス削減 / ネットワーク / 画像検索 / ワークフロー / 学習分類子 |
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| Research Abstract |
This study has proposed a novel method to develop a process response model from continuous time series data. The method consists of the following phasesm : (1) Reciprocal correlation analysis, (2) Process response model, (3) Extraction of control rules, (4) Extraction of a workflow, (5) Validation. The main contribution of the research is to establish a method to mine a set of meaningful control rules from Learning Classifier System using the Minimum Description Length criteria and Tabu search method. The proposed method has been applied to an actual process of a biochemical plant and has shown the validity and the effectiveness. We also conclude that advanced agent-based models are able to contribute to discover new knowledge in the fields of historical sciences.
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