基于直觉模糊可能性分布的三支决策模型的研究

直觉模糊集理论和可能性理论的融合是不确定问题领域的一个研究热点。文中提出了一种基于直觉模糊可能性分布的直觉模糊可能性测度(Intuitionistic Fuzzy Probability Measurement,IFPM),并在此基础上构建了三支决策模型。首先,定义了直觉模糊决策空间及该空间上的直觉模糊可能性分布,并对其性质进行了证明,给出了论域对象的隶属度和非隶属度可能性均值的计算方法。然后,讨论了论域对象的隶属度和非隶属度可能性均值与决策阈值的关系,分析了它们之间的概率分布情况。根据概率分布－可能性分布的转换关系,给出决策规则和三支决策模型,提出了一种基于直觉模糊可能性分布的IFPM决策风险计算方法。最后,考虑论域中对象的增减变化引起的IFPM变化,给出对应公式并对动态决策过程进行分析,同时通过实例验证了该模型的有效性。     

三枝决策粗糙集

从贝叶斯理论出发,介绍基于三枝决策粗集理论。首先讨论在期望风险最小决策的语义下决策粗集理论基本模型的构建过程。其次,分析决策粗集三枝决策方法在不同概率区间犯错的可能性,并通过其与二枝决策及Pawlak粗集三枝决策的差异,给出决策粗集三枝决策方法优于其他两种决策方法的成立条件。最后,提供一种利用决策粗集三枝决策解决实际问题的方法。     

作战指挥的可拓决策方法

作战决策是作战指挥的核心。论述了作战指挥中的决策问题,分析了决策思维的逻辑结构和作战指挥决策的内涵及特点。引入可拓学理论与方法对作战决策问题进行形式化描述,建立了基于可拓决策方法的作战决策方案生成与评价模型,并结合案例分析,说明了该方法的可行性和有效性。可应用于作战模拟与决策支持系统。     

基于Vague集模糊一致关系的多目标模糊决策

基于Vague集理论和模糊一致关系理论,提出了一种新的多目标模糊决策方法。这种决策方法综合考虑方案满足目标条件的可能性、不满足的可能性和未知的可能性三个方面的因素,分别对每个目标条件建立模糊优先关系矩阵和模糊一致矩阵,最后得出用于评估每种方案的真、假两方面的优度值,从支持和反对两方面来进行综合评估,给出方案的优劣程度排序,从而选出最优的方案。     

基于案例推理的应急辅助决策方法研究*

将案例推理的方法应用到应急决策中,为应急决策提供了一种实用的科学辅助方法。在分析了应急案例特征的基础上设计了一种基于概念树—突发事件本体模型—事件元模型三层架构的应急案例通用的案例描述与组织方法;根据应急案例属性复杂及属性值缺失的问题设计了基于结构相似度和属性相似度双层结构的案例全局相似度计算算法,避免了传统最近相邻算法中的属性值缺失问题;最后通过基于案例推理的应急辅助决策原型系统的开发使设计方法得以实现,证明了该方法具有较好的实用性。     

基于Vague集的模糊决策方法

目前在智能领域中对Vague集的研究已越来越广泛与深入．并运用于决策问题中．为了更加有效地进行模糊决策，提出了一个基于Vague集的模糊决策方法．在这个方法中，对约束条件，从它出现的可能性和不出现的可能性以及未知是否出现的可能性三个方面去综合处理．使得决策更加准确和有效．还给出了一个实例说明这种基于Vague集的模糊决策方法．这个基于Vague集的模糊决策方法的提出，为决策系统提供了一个有用的工具．     

基于模型的动态评估军事决策方案的方法研究

对军事决策方案(决心方案)的评估，是军事决策过程中的一个重要环节，其评估结论直接影响军事决策的结果。提高方案评估的科学性和可信度，是军事辅助决策迫切需要解决的问题。该文通过对军事决策方案及传统评估方法存在问题的分析，提出了基于模型的动态评估军事决策方案的方法，其核心是采用定性与定量分析方法的有机融合来实现对方案的评估，并提出实现的途径和重点需要解决的问题。     

基于案例推理的交通疏导辅助决策方法

为避免实际交通疏导问题中人为主观因素的影响,将案例推理理论应用于交通疏导决策中,提出一种基于案例推理的交通疏导决策方法,建立交通疏导案例库模型。区分属性数据类型,设计基于ID 3信息熵的案例相似度算法,案例匹配时可以兼顾特征属性与案例效果,避免传统算法中属性值缺失的问题。实例验证了该方法能够提高案例推理结果的准确度,对辅助处理交通疏导问题具有一定实际指导意义。     

基于贝叶斯网络约简的铁路应急决策方法

为了解决决策属性的冗余问题,降低决策推理过程的复杂性,实现在信息不完备情况下铁路应急决策的智能化,基于粗糙集理论与贝叶斯网络提出一种新的铁路应急决策方法。利用基于信息熵的粗糙集知识约简方法提取最小决策信息集,实现对应急态势信息集的约简,从而减少态势网络节点数目,降低贝叶斯网络的复杂性。基于约简后的贝叶斯网络模型实现了铁路应急态势预测的概率决策推理。案例分析表明该方法能够满足铁路应急决策需求以及在信息不完备条件下的有效性。     

三支决策代价目标函数的关系及推理研究

三支决策是不确定问题求解的重要理论。经典的决策粗糙集模型通过计算三支区域总体决策最小化风险,给出了一种有效的三支决策阈值求解方案。然而 对于决策粗糙集理论中代价目标函数之间的逻辑关系及其三支决策阈值间的推理 ,目前尚未有研究进行深入讨论。首先,提出了一种基于三支决策代价目标函数间逻辑关系的新型阈值计算方法。其次,根据不同损失函数取值分布情况下的三支决策阈值推导,分别给出了不同阈值的三支分类语义解释。最后,通过一组典型的实例证明了提出的基于三支决策代价目标函数的阈值计算方法及三支决策分类的推理是有效的。     

基于阈值的案例决策方法及其在创新设计中的应用

通过分析案例决策(CBDT)的经验理性,提出了可用于创新设计的案例决策方法,首先分析偏好反转的内在机理,指出CBDT中的案例对决策结果存在的潜在影响;然后提出一种判别无关案例和启发性案例的方法以及基于阈值的案例决策方法;最后通过摩托车发动机的产品创新设计的心用案例,说明了所提出方法的决策步骤.该方法与案例推理(CBR)方法相比,对于创新设计具有更好的实用价值和现实意义.     

Network representation with clustering tree features

This paper studies the relationship between a case-based decision theory (CBDT) and an ideal point model (IPM). We show that a case-based decision model (CBDM) can be transformed into an IPM under some assumptions. This transformation can allow us to visualize the relationship among data and simplify the calculations of distance between one current datum and the ideal point, rather than the distances between data. Our results will assist researchers with their product design analysis and positioning of goods through CBDT, by revealing past dependences or providing a reference point. Furthermore, to check whether the similarity function, presented in the theoretical part, is valid for empirical analysis, we use data on the viewing behavior of audiences of TV dramas in Japan and compare the estimation results under the CBDM that corresponds to a standard decision model with similarities and other various similarity functions and without a similarity function. Our empirical analysis shows that the CBDM with a similarity function, presented in this study, best fits the data.     

Learning and Herding Using Case-Based Decisions With Local Interactions

We evaluate repeated decisions of individuals using a variant of the case-based decision theory (CBDT), where individuals base their decisions on their own past experience and the experience of neighboring individuals. Looking at a range of scenarios to determine the successful outcome of a decision, we find that for learning to occur, agents must have a sufficient number of neighbors to learn from and access to sufficiently independent information. If these conditions are not fulfilled, we can easily observe herding in cases where no best decision exists.     

Fuzzy methods for case-based recommendation and decision support

The paper proposes two case-based methods for recommending decisions to users on the basis of information stored in a database. In both approaches, fuzzy sets and related (approximate) reasoning techniques are used for modeling user preferences and decision principles in a flexible manner. The first approach, case-based decision making, can principally be seen as a case-based counterpart to classical decision principles well-known from statistical decision theory. The second approach, called case-based elicitation, combines aspects from flexible querying of databases and case-based prediction. Roughly, imagine a user who aims at choosing an optimal alternative among a given set of options. The preferences with respect to these alternatives are formalized in terms of flexible constraints, the expression of which refers to cases stored in a database. As both types of decision support might provide useful tools for recommender systems, we also place the methods in a broader context and discuss the role of fuzzy set theory in some related fields.     

Case-based knowledge and induction

Case-based decision theory (CBDT) is a theory of decision-making under uncertainty, suggesting that people tend to choose acts that performed well in similar cases they recall. The theory has been developed from a decision-/game-/economic-theoretical point of view as a potential alternative to expected utility theory (EUT). In this paper, we attempt to reconsider CBDT as a theory of knowledge representation, to contrast it with the rule-based approach, and to study its implications regarding the process of induction     

基于数据挖掘和范例推理的智能分析决策支持技术综述

介绍了数据挖掘中的一些关键技术、人工智能基于范例推理、决策支持的主要理论及其发展,提出了范例推理、类比学习、规则推理之间的联系,详细探讨了数据挖掘技术、基于范例推理和决策支持理论集成的问题,最后对上述技术在预测领域的综合应用前景作了探讨。     

Diagnostic reasoning and medical decision-making with fuzzy influence diagrams

Influence diagrams have been widely used as knowledge bases in medical informatics and many applied domains. In conventional influence diagrams, the numerical models of uncertainty are probability distributions associated with chance nodes and value tables for value nodes. However, when incomplete knowledge or linguistic vagueness is involved in the reasoning systems, the suitability of probability distributions is questioned. This study intends to propose an alternative numerical model for influence diagrams, possibility distributions, which extend influence diagrams into fuzzy influence diagrams. In fuzzy influence diagrams, each chance node and value node is associated with a possibility distribution which expresses the uncertain features of the node. This study also develops a simulation algorithm and a fuzzy programming model for diagnosis and optimal decision in medical settings.     

铁路超限超重货物装载加固可拓实例推理方法

铁路超限超重货物具有长大、笨重和价值昂贵等特征,装载加固影响因素众多且无法完全量化表达,超限超重货物装载加固决策问题是一个半结构化问题,设计装载加固可拓实例推理技术对提升铁路超限超重货物安全装载水平和运输质量尤为重要。结合铁路超限超重货物特征及其装载加固决策要素,采用可拓基元与实例推理技术,构造超限超重货物装载加固实例推理基础数据与推理规则模块的可拓基元模型,分析装载加固可拓实例属性取值特征,给出局部与全局相似度计算公式,设计超限超重货物装载加固可拓实例推理算法,确定待解实例的解。实例运用表明所给出的可拓实例推理方法可制定出合理安全的装载加固方案,切实有效解决铁路超限超重货物装载加固决策问题。     

A case-based reasoning approach for building a decision model

A methodology based on case-based reasoning is proposed to build a topological-level influence diagram. It is then applied to a project proposal review process. The formulation of decision problems requires much time and effort, and the resulting model, such as an influence diagram, is applicable only to one specific problem. However, some prior knowledge from the experience in modeling influence diagrams can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem-solving experience to solve new problems.
In this paper, we suggest case-based decision class analysis (CB-DCA), a methodology based on case-based reasoning, to build an influence diagram. CB-DCA is composed of a case retrieval procedure and an adaptation procedure. Two measures are suggested for the retrieval procedure, one a fitting ratio and the other a garbage ratio. The adaptation procedure is based on decision-analytic knowledge and decision participants' domain-specific knowledge. Our proposed methodology has been applied to an environmental review process in which decision-makers need decision models to decide whether a project proposal is accepted or not. Experimental results show that our methodology for decision class analysis provides decision-makers with robust knowledge-based support.