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- Matsubayashi Shota
- Nagoya University, Graduate School of Informatics
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- Miwa Kazuhisa
- Nagoya University, Graduate School of Informatics
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- Terai Hitoshi
- Kindai University, Faculty of Humanity-oriented Science and Engineering
Bibliographic Information
- Other Title
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- 変則的挙動への認知的処理に関する記憶ベース方略の効用
- 変則的挙動への認知的処理に関する記憶ベース方略の効用 : ACT-Rによるモデルベースアプローチ
- ヘンソクテキ キョドウ エ ノ ニンチテキ ショリ ニ カンスル キオク ベース ホウリャク ノ コウヨウ : ACT-R ニ ヨル モデルベースアプローチ
- — Model-Based Approach with ACT-R
- ―ACT-Rによるモデルベースアプローチ
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Abstract
Users often observe anomalous behaviors of systems, such as machine failures, autonomous agents, and natural phenomena. We analyze the features and the benefits of the memory-based strategy, which focuses on memorization of instances to predict anomalous and regular behaviors of the system. In this study, we develop our previous research and investigate the cognitive processes and the benefits of the memory-based strategy with ACT-R model simulations. We set the parameters defining the encoding processes of anomalous instances and regular instances in the model of the memory-based strategy and performed simulations to verify how these two parameters influence prediction performance. The results of simulations showed that (1) anomalous instances are encoded and regular instances are not encoded in the memory-based strategy and that (2) such inactivity on regular instances suppresses commission errors of regular instances and does not suppress commission errors of anomalous instances and omission errors, which leads to correct prediction of systems' behaviors.
Journal
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- Cognitive Studies: Bulletin of the Japanese Cognitive Science Society
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Cognitive Studies: Bulletin of the Japanese Cognitive Science Society 26 (3), 332-342, 2019-09-01
Japanese Cognitive Science Society
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Details 詳細情報について
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- CRID
- 1390846609808832128
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- NII Article ID
- 130007804274
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- NII Book ID
- AN1047304X
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- ISSN
- 18815995
- 13417924
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- NDL BIB ID
- 029978584
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- CiNii Articles
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- Abstract License Flag
- Disallowed