Non-monotonic logic in artificial intelligence and Its Applications

On the Non-monotonic Logical Nature of Pragmatic Inference in Conversational Implicature

会话含义推导中的非单调逻辑属性

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This paper attempts to examine the inferential process of conversational implicature in the light of non-monotonic logic, study the issue of how non-monotonic logic can be employed to cancel the conversational implicature and make use of plausible reasoning and default reasoning to infer the conversational implicature.

本文试图从非单调逻辑的角度来探讨会话含义的推导过程，运用非单调逻辑来解释会话含义的可取消性特征，使用合情推理、缺省推理来推导会话含义。

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This paper proposes a non-monotonic logic which can be used to analyze the properties of security protocols.

本文提出一种对安全协议进行分析的非单调逻辑。

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The Relation between Counterfactual Thinking and Causal Reasoning

反事实思维与因果推理的关系

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The results indicated: ( 1) The scores of 3-year-old children's consequent counterfactual reasoning were lower than that of 4 and 5-year-old children. But there was no significant difference between 4 and 5-year-old children's consequent counterfactual reasoning.

结果表明：1.3岁儿童在结果反事实推理中的得分显著低于4岁和5岁儿童，而4岁和5岁儿童的得分并不存在显著差异；

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Data were collected to a sample of 58 3-to 5-year-old children to investigate the development of their counterfactual thinking using consequent counterfactual tasks and antecedent counterfactual tasks.

该研究采用结果和前提反事实推理任务，选取58名3-5岁幼儿为被试，考察了其反事实思维能力的发展。

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On an Effect Produced by Counterfactual Reasoning in Different Story Situations on Emotion

不同故事情境下的反事实推理对情绪影响的实验研究

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The Analysis of Children's Errors in Counterfactual Reasoning

幼儿反事实推理的错误源分析

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Development of Young Children's Antecedent Counterfactual Reasoning and Its Influencing Factors

幼儿前提反事实推理的发展特点和影响因素

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The paper presents a Bayesian networks ( BN) framework for the reliability analysis of phased-mission systems ( PMS), named PMS-BN model.

针对多阶段系统（PMS）的可靠性评估问题，提出了一种基于贝叶斯网络（BN）的可靠性分析模型PMS-BN。

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Then residuals of multiple MIB variables are combined in the probabilistic framework of a Bayesian network to compute the probability of fault, including unknown or unpredictable faults.

把观测得到的各MIB变量的信息经由贝叶斯网络加以融合，从而计算出网络出现故障的概率，包括未知的和不可预见的故障。

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Due to the shortcomings of the randomness and uncertainty of power transformer fault diagnosis data, the benefits of the Bayesian network classifiers and the features of multi-Agent, this paper introduces a multi-Agent system diagnosis model.

电力变压器数据信息具有不完备、不确定性的缺点，而贝叶斯网络有知识表达灵活、分析处理不确定性与关联性问题能力强的优点，本文提出一种基于Multi-Agent的变压器故障诊断模型。

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Experimental results under the same train and test data show that this network structure can achieve thirty percent average relative improvement in performance than classical Gaussian mixture model.

通过与传统的混合高斯模型（GMM）的实验类比，该贝叶斯网络模型能够在同样的训练和测试数据情况下识别率平均相对提高30%。

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The traditional Markov Chain Monte Carlo method for structural learning in graphical models MCMC algorithm is improved.

改进了关于图模结构学习中常见的MCMC算法。将这种方法构造的贝叶斯网络作为马尔可夫链初始状态的网络结构，利用改进后的MCMC算法，构造一个关于贝叶斯网络结构的马尔可夫链。

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However, reasoning on student model is a NP hard question in fact, since undirected loops are embedded in the Bayesian student model.

由于建立好的贝叶斯网络学生模型中存在无向环，使学生模型的推理成为一个NP难题。

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We constructed student model with Bayesian Nets and Granularity Hierarchies method. They provide basis of strategy of test and student's adaptive learning.

采用贝叶斯网络（Bayesian Nets）和粒度层次（Granularity Hierarchies）方法构建了覆盖型学生模型，为知识前测的出题策略和系统的学生适应性学习提供了依据。

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Study on Threat Evaluation Based on OWA Operator and Bayesian Network

基于OWA算子的贝叶斯网络空地战场威胁评估研究

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This paper also builds an integrated experimental platform for the test and further research of Bayesian Networks, and the improved algorithm is applied on the reliability assessment of power system.

net和XML技术实现了贝叶斯网络综合实验平台的构建，对上述算法加以实现，并应用到电力系统可靠性评估中，取得了比较好的效果。

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Bayesian network research mainly focused on the learning of Bayesian networks, especially the structure learning of Bayesian network.

贝叶斯网络的研究主要在贝叶斯网络的学习上，特别是贝叶斯网络的结构学习，而现在的研究表明贝叶斯网络的结构学习是NP问题，因此有效的结构学习方法的探寻是贝叶斯网络理论研究的重点。

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Many methods have been used in data mining, Bayesian networks has become a focus currently. It has the broad application prospects.

常用的数据挖掘方法有许多，贝叶斯网络（Bayesian Networks，BN）方法在数据挖掘中的应用是当前研究的热点问题，具有广阔的应用前景。

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