基于论据系统的带权定性概率推理机

定性概率推理是不确定性推理领域的一种重要方法。将定性概率推理的论据系统方法和抽象系统方法二者合而为一,在定性概率推理机(QPR)的基础上提出基于论据系统的带权定性概率推理机(WQPR)。首先扩展了带权定性概率网的定义,讨论了带权定性影响的对称性;其次将带权定性概率推理融入到论据系统中,提出WQPR推理系统,相比QPR能够在更精确的尺度进行不确定性推理,并证明了系统的正确性与完备性。     

基于粗糙集的定性概率网推理冲突解决方法

定性概率是贝叶斯网的定性抽象,它以有向边上的定性影响代替贝叶斯网中的条件概率参数,描述了变量间增减的趋势,具有高效的推理机制。但定性概率网中信息丢失导致推理的过程中往往产生不确定信息,即推理结果产生冲突。以尽可能消除定性推理中的冲突为出发点,在构建定性概率网时,基于粗糙集属性依赖度理论求解出网中节点间的依赖度,以依赖度作为变量间定性影响的权重,并根据依赖度改进已有的定性概率网推理算法,从而解决定性概率网推理冲突。实例验证表明,该方法既保持了定性概率网高效推理的特性,又能有效解决冲突。     

带权定性影响图

定性影响图是具有精确概率和效用的影响图的定性抽象。在定性影响图中,节点之间的影响关系使用定性符号描述,这种符号描述简化了不确定知识的表示,降低了影响图建模的难度,加速了不确定知识的推理。但是,定性影响图在抽象过程中损失了部分信息,导致定性影响图在评价过程中会产生不确定结果,阻碍了定性影响图的广泛应用。以加权的思想扩展定性影响图,使扩展的定性影响图中每个定性影响都带有一个表示节点间影响强弱的数值权值,在评价过程中根据定性影响权值的比较来减少不确定结果的产生,从而扩大定性影响图的应用范围。     

带权重的定性贝叶斯网

定性贝叶斯网使用定性的影响标记表示变量之间的影响关系,但不能表达影响力的强弱,在推理过程中容易出现不确定的结果.研究了带权重的定性贝叶斯网,权重是一个[0,1]区间内的数值,用以表达影响力的强弱,修正了推理中的运算规则,并设计了权重标杆方便专家给出权重,保持了定性贝叶斯网络建模过程的简易性,同时提高了推理精度.     

定性影响图及其决策算法改进研究

影响图这项图形决策技术是解决现实决策问题的有效手段之一.但是,需要获取大量概率和效用值成为了其应用的主要障碍.当事物的依赖关系或者相关参数值无法精确描述时,如何利用非精确的信息进行推理已成为提高影响图决策效率的关键问题.为了在一定程度上克服这种障碍,定性贝叶斯网络及其相关算法被提出并得到发展.本文在分析定性影响图相关理论的基础上,对定性影响图决策推理算法进行了有效改进.     

基于Voronoi图的定性路径推理

动态空间知识的表示与推理是定性空间推理研究的重要内容.基于Voronoi图及其动态变化,提出运动路径定性表示与推理方法.先根据Voronoi图空间邻近关系定义Voronoi图生成子空间关系,进一步定义定性位置及概念邻域,并应用概念相邻的定性位置序列给出定性路径表示.再由动态Voronoi图的边集变化和给出的概念邻域中定性位置间最短路径的启发式算法,设计并实现具有观察者角度的定性路径推理算法.最后,实验分析并验证该方法的有效性.     

基于定量定性互信息的多层次特征图像匹配算法

针对传统基于互信息图像匹配算法计算量大,且没有考虑像素空间关系和效用的问题,提出了一种基于定量定性互信息的多层次特征图像匹配算法：首先对边缘提取后的图像提取多层次特征,即边缘兴趣点、边缘点和边缘邻域点特征;然后基于不同特征点特性,计算定量定性互信息联合效用;最后在遗传算法框架下,将定量定性互信息值作为适应度函数值搜索匹配参数。仿真结果表明,本文提出的匹配算法精度高,耗时较短且对噪声不敏感。     

基于定性状态模型的定性控制方法研究

针对某些复杂系统难以获得精确数学模型来实现有效控制这一问题,采用定性状态模型来描述含有不确定性的非线性系统,提出了一种定性控制方法。首先通过对系统状态的定性划分,采用基于定性推理的自动机建立被控系统定性状态模型,然后设计基于系统定性状态模型的定性控制器,并给出了控制器设计的一般步骤。最后以液位控制系统为例,建立了液位对象的定性状态模型并设计了定性控制器,仿真结果表明该方法的有效性及其理论价值和应用前景。     

基于Voronoi图的定性路径

定性路径是定性空间推理的一个基本概念。给出了一个基于Voronoi图的定性路径表示与推理方法。该方法应用Voronoi图的邻近关系来表示定性位置和定性路径,即用运动点所在Voronoi区域的邻域来表示定性位置,用运动点所经过的定性位置序列来表示定性路径。设计并实现了一个定性路径推理算法,基于初始Voronoi图及不同时刻所有Voronoi区域的边数来动态更新Voronoi图邻近关系,可识别出运动点并找出定性路径。实验结果表明,该方法是可行的。     

基于定性模型的定性仿真方法

定性仿真在定性推理中起着核心作用．本文将介绍基于定性模型的定性仿真法的基本内容，讨论支持其走向实用的主要方法和技术，并展示其在若干领域中的应用．     

Qualitative probabilistic networks with reduced ambiguities

A Qualitative probabilistic network (QPN) is the qualitative abstraction of a Bayesian network that encodes variables and the qualitative influences between them. In order to make QPNs be practical for real-world representation and inference of uncertain knowledge, it is desirable to reduce ambiguities in general QPNs, including unknown qualitative influences and inference conflicts. In this paper, we first extend the traditional definition of qualitative influences by adopting the probabilistic threshold. In addition, we introduce probabilistic-rough-set-based weights to the qualitative influences. The enhanced network so obtained, called EQPN, is constructed from sample data. Finally, to achieve conflict-free EQPN inferences, we resolve the trade-offs by addressing the symmetry, transitivity and composition properties. Preliminary experiments verify the correctness and feasibility of our methods.     

Context-specific sign-propagation in qualitative probabilistic networks

Qualitative probabilistic networks are qualitative abstractions of probabilistic networks, summarising probabilistic influences by qualitative signs. As qualitative networks model influences at the level of variables, knowledge about probabilistic influences that hold only for specific values cannot be expressed. The results computed from a qualitative network, as a consequence, can be weaker than strictly necessary and may in fact be rather uninformative. We extend the basic formalism of qualitative probabilistic networks by providing for the inclusion of context-specific information about influences and show that exploiting this information upon reasoning has the ability to forestall unnecessarily weak results.     

Quantitative Inference by Qualitative Semantic Knowledge Mining with Bayesian Model Averaging

In this paper, we consider the problem of performing quantitative Bayesian inference and model averaging based on a set of qualitative statements about relationships. Statements are transformed into parameter constraints which are imposed onto a set of Bayesian networks. Recurrent relationship structures are resolved by unfolding in time to Dynamic Bayesian networks. The approach enables probabilistic inference by model averaging, i.e. it allows to predict probabilistic quantities from a set of qualitative constraints without probability assignment on the model parameters. Model averaging is performed by Monte Carlo integration techniques. The method is applied to a problem in a molecular medical context: We show how the rate of breast cancer metastasis formation can be predicted based solely on a set of qualitative biological statements about the involvement of proteins in metastatic processes.     

Enhanced qualitative probabilistic networks for resolving trade-offs

Qualitative probabilistic networks were designed to overcome, to at least some extent, the quantification problem known to probabilistic networks. Qualitative networks abstract from the numerical probabilities of their quantitative counterparts by using signs to summarise the probabilistic influences between their variables. One of the major drawbacks of these qualitative abstractions, however, is the coarse level of representation detail that does not provide for indicating strengths of influences. As a result, the trade-offs modelled in a network remain unresolved upon inference. We present an enhanced formalism of qualitative probabilistic networks to provide for a finer level of representation detail. An enhanced qualitative probabilistic network differs from a basic qualitative network in that it distinguishes between strong and weak influences. Now, if a strong influence is combined, upon inference, with a conflicting weak influence, the sign of the net influence may be readily determined. Enhanced qualitative networks are purely qualitative in nature, as basic qualitative networks are, yet allow for resolving some trade-offs upon inference.     

一个选择最大似然互信息特征的网络关系预测通用模型

信息网络结构特征作为影响关系生成与演化的主要因素在信息网络关系分类与推断领域占据重要地位。现有的关系分类与推断算法在处理网络结构特征的过程中,无法达到令人满意的效果。为此,结合互信息的定义,提出一种基于互信息特征选择的关系分类与推断算法。通过定义CN、AA、Katz等相似度指标充分抽取局部和全局(半全局)两类网络结构特征,利用基于密度比函数的最大似然估计来计算特征之间的近似互信息。该密度函数有效地解决了特征选择中全局最优解的过程,同时筛选出更具判别性的特征。通过多个真实信息网络数据集上的实验结果表明,无论是经典分类算法还是新近提出的基于学习理论的关系分类算法,经过互信息特征选择步骤的算法在Accuracy、AUC、Precision等评价指标上均比基准算法要优。     

An Approximate Algorithm for Min‐Based Possibilistic Networks

Min‐based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min‐based networks, which consists of propagating information through the network structure to answer queries. Exact inference computes posteriori possibility distributions, given some observed evidence, in a time proportional to the number of nodes of the network when it is simply connected (without loops). On multiply connected networks (with loops), exact inference is known as a hard problem. This paper proposes an approximate algorithm for inference in min‐based possibilistic networks. More precisely, we adapt the well‐known approximate algorithm Loopy Belief Propagation (LBP) on qualitative possibilistic networks. We provide different experimental results that analyze the convergence of possibilistic LBP.     

面向不精确与非量值信息的定性数字样机

为了解决定量仿真不能很好利用系统中的不精确和非量值信息的问题,将Kuipers定性仿真理论与数字样机技术相结合,使数字样机能够使用定性与定量信息.提出了定性数字样机的概念,并以四元组形式对定性数字样机进行定义,建立定性数字样机的概念框架.根据定性数字样机特性选择Prolog语言和面向对象的知识表示方法,对定性数字样机系统的知识描述及系统框架的建立进行了阐述.建立了定性数字样机的知识结构表示框架与结构模型,通过定性仿真的步骤与方法,对定性数字样机进行了仿真验证,通过实例说明了使用定性数字样机进行系统仿真的可行性.     

基于关联假设的定性组合建模算法

组合建模是基于建模假设和模型片断库，构造定性仿真模型的自动化方法。关联假设是一类模型假设，它既可以用来定义模型片断本身，也可以定义有关模型片断使用的决策信息，将模型库与建模任务联系起来。在知识分类的基础上，该文提出了基于关联假设的模型组合算法，解决了定性仿真模型的构造问题，主要内容包括关联性的表示、关联推理算法及其具体实现，最后给出一个实例。