| 因果关系,贝叶斯网络与认知图 |
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| 引用本文： | 刘志强.因果关系,贝叶斯网络与认知图[J].自动化学报,2001,27(4):552-566. |
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| 作者姓名： | 刘志强 |
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| 作者单位： | 1.墨尔本大学计算机科学与软件工程系智能系统实验室,澳大利亚 |
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| 摘 要： | 因果关系在预测和推理中具有重要的作用.贝叶斯网络已被用于构建诊断和决策系
统.近年来模糊认知图得到了重视.模糊认知图为结构性知识与因果推理提供了又一个理论
框架.本文简单介绍贝叶斯网络与认知图及其推理方法在智能系统中的应用.
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| 关 键 词： | 贝叶斯网络 模糊认知图 因果关系 推理 决策 动态系统 智能系统 |
| 收稿时间： | 2000-8-20 |
| 修稿时间： | 2000年8月20日 |
Causation,Bayesian Networks,and Cognitive Maps |
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| LIU Zhi-Qiang.Causation,Bayesian Networks,and Cognitive Maps[J].Acta Automatica Sinica,2001,27(4):552-566. |
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| Authors: | LIU Zhi-Qiang |
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| Affiliation: | 1.Intelligent Systems Lab.(ISL),Department of Computer Scinece and Software Engineering,University of Melbourne,VIC 3010,Australia |
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| Abstract: | Causation plays a critical role in many predictive and inference tasks. Bayesian networks (BNs) have been used to construct inference systems for diagnostics and decision making. More recently, fuzzy cognitive maps (FCMs) have gained consid- erable attention and offer an alternative framework for representing structured human knowledge and causal inference. In this paper I briefly introduce Bayesian networks and cognitive networks and their causal inference processes in intelligent systems. |
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| Keywords: | Bayesian networks fuzzy cognitive map causation inference decision making dynamic systems intelligent systems graphs |
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