基于大型模糊认知图的复杂系统建模与推理研究

模糊认知图作为一种智能计算工具,具有直观的知识表达、快速的数值推理能力等优点,适用于系统建模与推理.为了实现复杂系统关联认知与聚类的集成挖掘,进而达到对它的有效分析与决策,在面向复杂系统的大型模糊认知图研究基础之上,提出了用于三江源生态决策的大型模糊认知图关联认知、状态建模与推理的思路与方法,它不仅可以提升大型模糊认知图在复杂系统建模与推理方面的理论研究,也将拓展其应用领域.     

基于不确定性知识的主观贝叶斯网络算法

1引言 贝叶斯网络是有向无环图.它是一种概率推理技术,能从不完全、不精确或不确定的知识和信息中作出推理.主观贝叶斯网络引入了主观贝叶斯方法,成功地解决了在实际应用中的诸多困难.     

基于相似粗糙集和模糊认知图的文本分类研究

阐述了基于相似粗糙集和模糊认知图的文本分类问题,提出了一种基于模糊认知图的文本分类推理算法,使文本分类成为一个基于文本特征项的权和特征项与类别的相关度构成的模糊认知图进行推理的结果,最后对该算法进行了实验,并对结果进行了分析.     

认知图理论的应用研究

认知图又称心象图,是近年来研究的热点.它是一种定性推理技术,也可以看作是一种计算智能,能有效地解决基于先验知识的自适应行为.文中对认知图理论作了系统的归纳总结,主要包括古典认知图和模糊认知图两方面,模糊认知图是认知图的延伸与扩展.文中通过对模糊认知图扩展模型的描述以及扩展模型之间优缺点的比较,得出了模糊认知图扩展模型的实用性和优越性.     

概率图模型推理方法的研究进展

近年来概率图模型已成为不确定性推理的研究热点,在人工智能、机器学习与计算机视觉等领域有广阔的应用前景.根据网络结构与查询问题类型的不同,系统地综述了概率图模型的推理算法.首先讨论了贝叶斯网络与马尔可夫网络中解决概率查询问题的精确推理算法与近似推理算法,其中主要介绍精确推理中的VE算法、递归约束算法和团树算法,以及近似推理中的变分近似推理和抽样近似推理算法,并给出了解决MAP查询问题的常用推理算法;然后分别针对混合网络的连续与混合情况阐述其推理算法,并分析了暂态网络的精确推理、近似推理以及混合情况下的推理;最后指出了概率图模型推理方法未来的研究方向.     

整合贝叶斯网络的BDI模型

针对BDI模型在不确定领域应用的薄弱,提出了一种改进的BDI(Beliefs—Desires—Intentions)模型。以BDI体系结构为基础,结合贝叶斯网络,建立了智能体对可能世界的认知表示;通过贝叶斯网络推理,实现了对达成目标状态的期望估计;在意图中,通过引入规划因子,完成行为决策。用Java语言在eclipse平台上对农作物栽培实例进行仿真,验证了模型能够在不确定环境中,进行理性的认知、推理、规划。     

模糊认知图在时间序列预测中的应用综述

时间序列预测是基于当前及历史数据对未来演化趋势的推演.准确的、可解释的时间序列预测是进行科学决策的关键技术支撑,广泛应用于金融、交通、气象等诸多领域.具有可解释性和强推理能力的模糊认知图已在时间序列预测中取得较好的效果,但目前尚无文献对该方法进行全面综述.为此,本文首先对模糊认知图及扩展的高阶模糊认知图、直觉模糊认知图和深度模糊认知图进行梳理,并在此基础上归纳了学习模糊认知图的优化算法.其次,具体介绍了模糊认知图以及扩展的模糊认知图在时间序列预测中的应用,并做出系统性的总结.最后,对模糊认知图在时间序列预测中的发展趋势进行展望.     

基于模糊贝叶斯网的危害性分析方法

针对传统的故障模式、影响与危害性分析(FMECA)方法不足的问题,提出了一个基于模糊贝叶斯网的危害性分析方法。该方法将模糊理论与贝叶斯网推理技术结合起来,用三角模糊数来描述专家的模糊评分值;通过模糊集合映射,将其转化为评级的模糊子集;以置信结构的模糊规则,表示故障模式的属性与危害度之间的关系;利用贝叶斯网络推理算法综合置信结构的模糊规则,通过贝叶斯网推理得到模糊子集形式的危害度,再经过去模糊计算,得到故障危害等级的清晰值,从而确定故障模式的危害程度。实验结果表明,所提方法能够提高传统分析方法的准确性和应用范围。     

基于有序加权平均算子的概率模糊认知图

模糊认知图(FCM)与概率模糊认知图(PFCM)使用简单的加权和集结因果推理结果,忽略了原因节点间关联关系的不确定性,阈值函数导致推理结果进一步失真.在继承FCM与PFCM优点的基础上,引入有序加权平均(OWA)算子模拟各种确定的或模糊的与或组合关系,提出了基于有序加权平均算子的概率模糊认知图(OWA-PFCM).通过构建一个动态的攻击效能评估模型,阐述了OWA-PFCM在工程建模中的应用.OWA-PFCM能同时表示因果节点状态的不确定性、因果联系强度的不确定性、与或组合关系的不确定性,具有更强的模拟能力.     

基于结构分析的局部Gibbs抽样自动推理算法

提出一种基于结构分析的局部Gibbs抽样的贝叶斯网络推理算法(S-LGSI).S-LGSI算法基于联合树算法的概率图模型分析思想,对贝叶斯网络进行精确分解,然后根据查询结点和证据结点生成具有强相关性的局部网络模型,进而对局部网络模型进行Gibbs抽样推理.与当前基于抽样的其它近似推理算法相比,该算法降低推理的计算维数.同时,由于局部抽样模型包含了与查询结点相关的重要信息,因此该算法保证局部抽样推理的精度.算法分析和在Alarm网的实验结果表明,S-LGSI算法较显著降低时间复杂度,同时也提高推理精度.S-LGSI算法应用于上海证券交易所股票网络的推理结果与实际情况基本一致,表现出较强的实用性.     

Backward Inference in Bayesian Networks for Distributed Systems Management

The growing complexity of distributed systems in terms of hardware components, operating system, communication and application software and the huge amount of dependencies among them have caused an increase in demand for distributed management systems. An efficient distributed management system needs to work effectively even in face of incomplete management information, uncertain situations, and dynamic changes. In this paper, Bayesian networks are proposed to model dependencies between managed objects in distributed systems. The strongest dependency route (SDR) algorithm is developed for backward inference in Bayesian networks. The SDR algorithm can track the strongest causes and trace the strongest routes between particular effects and its causes, the strongest dependency of causes can be also achieved by the algorithm. Thus, the backward inference provides an efficient mechanism in fault locating, and is beneficial for performance management.     

On the combination of logical and probabilistic models for?information analysis

Formal logical tools are able to provide some amount of reasoning support for information analysis, but are unable to represent uncertainty. Bayesian network tools represent probabilistic and causal information, but in the worst case scale as poorly as some formal logical systems and require specialized expertise to use effectively. We describe a framework for systems that incorporate the advantages of both Bayesian and logical systems. We define a formalism for the conversion of automatically generated natural deduction proof trees into Bayesian networks. We then demonstrate that the merging of such networks with domain-specific causal models forms a consistent Bayesian network with correct values for the formulas derived in the proof. In particular, we show that hard evidential updates in which the premises of a proof are found to be true force the conclusions of the proof to be true with probability one, regardless of any dependencies and prior probability values assumed for the causal model. We provide several examples that demonstrate the generality of the natural deduction system by using inference schemes not supportable directly in Horn clause logic. We compare our approach to other ones, including some that use non-standard logics.     

多模块贝叶斯网络中推理的简化

多模块贝叶斯网络(MSBN)引入了模块化和面向对象思想，是复杂大系统建模的有力工具．目前，如何简化MSBN中局部和全局推理的时空复杂度已成为影响其应用的关键问题．首先分析了用于局部贝叶斯网络推理的两类经典算法的时空复杂度，证明了它们本质上的一致性，并给出了统一的理论解释；进而用实验证明了影响推理复杂度的决定性因素是网络模型相应导出图的导出宽度，并指出了可以精确推理的贝叶斯网络族．最后，分析了降低MSBN全局推理复杂度的可行性，给出了简化MSBN全局推理的指导性原则．     

变结构动态贝叶斯网络的机制研究

传统的动态贝叶斯网络(Dynamic Bayesian networks, DBNs)描述的是一个稳态过程,而处理非稳态过程,变结构动态贝叶斯网络更适 用、更灵活、更有效.为了克服现有变结构离散 动态贝叶斯网络推理算法只能处理硬证据的缺陷,本文在深入分析变结构动态贝叶斯网络机制及其特 征的基础上,提出了变结构离散动态贝叶斯网络的 快速推理算法.此外,对变结构动态贝叶斯网络的特例,即数据缺失动态贝叶斯网络进行了定义并构建 了相应的模型.仿真实验验证了变结构离散动态贝 叶斯网络快速推理算法的有效性及计算效率.     

Distributed Bayesian diagnosis for modular assembly systems—A case study

The growing interest in modular and distributed approaches for the design and control of intelligent manufacturing systems gives rise to new challenges. One of the major challenges that have not yet been well addressed is monitoring and diagnosis in distributed manufacturing systems. In this paper we propose the use of a multi-agent Bayesian framework known as Multiply Sectioned Bayesian Networks (MSBNs) as the basis for multi-agent distributed diagnosis in modular assembly systems. We use a close-to-industry case study to demonstrate how MSBNs can be used to build component-based Bayesian sub-models, how to verify the resultant models, and how to compile the multi-agent models into runtime structures to allow consistent multi-agent belief update and inference.     

The role of probability-based inference in an intelligent tutoring system

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs). Basic concepts of the approach are briefly reviewed, but the emphasis is on the considerations that arise when one attempts to operationalize the abstract framework of probability-based reasoning in a practical ITS context. The discussion revolves around HYDRIVE, an ITS for learning to troubleshoot an aircraft hydraulics system. HYDRIVE supports generalized claims about aspects of student proficiency through probabilitybased combination of rule-based evaluations of specific actions. The paper highlights the interplay among inferential issues, the psychology of learning in the domain, and the instructional approach upon which the ITS is based.     

Method of probabilistic inference from learning data in Bayesian networks

Bayesian networks (BN) are a powerful tool for various data-mining systems. The available methods of probabilistic inference from learning data have shortcomings such as high computation complexity and cumulative error. This is due to a partial loss of information in transition from empiric information to conditional probability tables. The paper presents a new simple and exact algorithm for probabilistic inference in BN from learning data. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 93–99, May–June 2007.     

Simplicity and Robustness of Fast and Frugal Heuristics

Intractability and optimality are two sides of one coin: Optimal models are often intractable, that is, they tend to be excessively complex, or NP-hard. We explain the meaning of NP-hardness in detail and discuss how modem computer science circumvents intractability by introducing heuristics and shortcuts to optimality, often replacing optimality by means of sufficient sub-optimality. Since the principles of decision theory dictate balancing the cost of computation against gain in accuracy, statistical inference is currently being reshaped by a vigorous new trend: the science of simplicity. Simple models, as we show for specific cases, are not just tractable, they also tend to be robust. Robustness is the ability of a model to extract relevant information from data, disregarding noise.Recently, Gigerenzer, Todd and the ABC Research Group (1999) have put forward a collection of fast and frugal heuristics as simple, boundedly rational inference strategies used by the unaided mind in real world inference problems. This collection of heuristics has suggestively been called the adaptive toolbox. In this paper we will focus on a comparison task in order to illustrate the simplicity and robustness of some of the heuristics in the adaptive toolbox in contrast to the intractability and the fragility of optimal solutions. We will concentrate on three important classes of models for comparison-based inference and, in each of these classes, search for that to be used as benchmarks to evaluate the performance of fast and frugal heuristics: lexicographic trees, linear modes and Bayesian networks. Lexicographic trees are interesting because they are particularly simple models that have been used by humans throughout the centuries. Linear models have been traditionally used by cognitive psychologists as models for human inference, while Bayesian networks have only recently been introduced in statistics and computer science. Yet it is the Bayesian networks that are the best possible benchmarks for evaluating the fast and frugal heuristics, as we will show in this paper.     

Sensitivity analysis on causal chains of Bayesian networks

To analyze the key path of Bayesian network in complex systems, this study proposes to analyze the sensitivity of causal chains of Bayesian networks using the Petri net structural analysis approach to obtain the key chain through which the cause influences the consequence. First, the Bayesian network is transformed into Petri net, the structural analysis approach of which is employed to analyze structural nature of the Bayesian network, ensuring correctness of the constructed Bayesian network structure. Then based on the above fact that the structure is correct, S‐invariants of a Petri net is used to search for simple causal chains of the Bayesian network. Finally, the causal effect is defined and sensitivity analysis is made on the causal chains. The said method is applied to MDS causal chain analysis. Results show that the proposed method is direct viewing and practical. This method has some reference value for decision making in complex systems. © 2011 Wiley Periodicals, Inc.     

Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models

Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the posterior expected utility. Experimental confirmation of the models, however, has been claimed because of groups of agents that “probability match” the posterior. Probability matching only constitutes support for the Bayesian claim if additional unobvious and untested (but testable) assumptions are invoked. The alternative strategy of weakening the underlying notion of rationality no longer distinguishes the Bayesian model uniquely. A new account of rationality—either for inference or for decision-making—is required to successfully confirm Bayesian models in cognitive science.