条件事件代数研究综述

综述了条件事件代数理论的原理、主要性质和应用．条件事件代数是一门新兴的解决不确定性、概率性和模糊性推理问题的学科,是在确保规则概率与条件概率相容的前提下,把布尔代数上的逻辑运算推广到条件事件(规则)集合中得到的代数系统,目的是为智能系统中的条件推理建立一个数学基础．该文也对比条件事件代数更一般的逻辑系统——关联事件代数理论进行了介绍．     

条件事件代数在专家系统中的应用研究

条件事件代数是在确保规则概率与条件概率相容的前提下,把布尔代数上的逻辑运算推广到条件事件（规则）集合中的逻辑代数系统,本文介绍了条件事件代数的基本原理和性质,并利用条件事件代数解决了专家系统的规则循环问题,克服了传统谓词逻辑在推理过程中的局限性。     

GNW条件事件代数的原理和应用

介绍了Goodman等提出的GNW条件事件代数的原理和性质，该代数系统在三值逻辑基础之上，在确保规则概率与条件概率相容的前提下，把布尔代数上的逻辑运算推广到条件事件（规则）集合中。     

InforSIB事件代数的形式化框架与代数性质

复合事件处理系统多节点处以及系统外部生成的大量的、连续到达的事件,通过规则匹配、推理等方法对事件模式进行检测,连续输出经过组合后的复合事件,以触发相应的后继处理。事件查询语言是规约事件模式的声明性语言,事件代数决定了事件查询语言的表达能力,同时也间接影响了事件检测的性能。因此,在权衡检测性能与事件查询语言的表达能力时,深入分析事件代数是非常重要的手段。基于White事件代数,扩展了事件代数的形式化框架,在形式化框架中考虑复合事件在更精细的时间关系下的语义定义,以增强事件代数的表达能力,同时考虑与应用相关的事件选择和消费策略-上下文策略,以提高事件检测的性能。讨论了InforSIB事件代数的代数性质,提出的上下文策略非常适合用于实时监控等领域,且具有良好的代数性质,保证了复合事件检测的时间和空间复杂度是有界的。     

离散事件动态系统的代数理论及其应用——现状与展望

确定性、无决策的离散事件动态系统(DEDS)可以表述为代数系Dioid上的线性模型并由此进行一些有效的分析。本文首先对Dioid理论做了简单介绍,综述了Dioid上发展起来的系统论及控制论的研究成果,对代数方法在计算机集成制造系统(CIMS)中的应用、现存问题和发展方向进行了探讨。     

基于信息论的Bayesian网络结构学习算法研究

Bayesian网是一种进行不确定性推理的有力工具,它结合图型理论和概率理论,可以方便地表示和计算我们感兴趣的事件概率,同时也是对实体之间依赖关系提供了一种紧凑、直观、有效的图形表示。文中基于信息论中测试信息独立理论,对Bayesian网中各结点进行条件独立(CI)测试,以发现各结点的条件依赖关系,并通过计算结点之间的互相依赖度以发现Bayesian网边的方向,从而构造Bayesian网结构,算法的计算复杂度只需要进行O(N²)次CI测试。     

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。     

融合多种算法的堆垛机诊断专家系统

针对堆垛机设备在运行过程中呈现的复杂性、不确定性等问题,设计了基于故障树和贝叶斯网络的混合诊断专家系统。采用故障树分析技术对堆垛机进行故障建模,得到最小割集,建立了以规则为知识表示形式的规则库。根据输入的故障征兆系统自动寻找匹配的故障事实库,建立了以该事件作为顶事件的故障树,并转化得到相应的贝叶斯网络,形成了基于规则的推理和贝叶斯网络的概率计算混合诊断机制。该方法有效利用了故障树分析和贝叶斯网络两种算法的优势,为复杂机器的故障诊断提供了一种新途径。试验表明,该系统有效解决了传统诊断专家系统存在的推理模式单一、知识获取困难等问题。概率计算混合诊断机制是一种快速诊断堆垛机的可行方式。     

一种带隐变量贝叶斯网的事件诱因估计方法

事件诱因是诱导事件发生的因素,从事件特征数据构建事件诱因模型,进行事件诱因估计,是解决舆论控制、精准决策支持和用户行为定向等问题的重要基础.本文以公共突发事件为背景,以贝叶斯网为不确定性知识表示和推理的基本框架,以多值隐变量来描述事件诱因的多个取值,提出一种基于带隐变量贝叶斯网(隐变量模型)的事件诱因模型构建方法,进而利用概率推理算法估计事件诱因.针对事件诱因存在多个取值的问题,本文基于分支限界思想提出最优取值子集提取算法.建立在真实数据集上的实验结果表明,本文提出的事件诱因模型构建方法及相应的诱因估计方法是有效的.     

一种用矩形代数表示主方向关系的方法

论文重点介绍了基于主方向物体MBR(MinimumBoundRectangle)与矩形代数(Rectanglealgebra)理论相结合对物体空间方向关系进行表述的一个新型模型。通过将物体方向和矩形代数有机结合,利用矩形代数良好的计算性质可以为以后的主方向空间推理以及一致性检验提供更为简便快捷的算法,为GIS和人工智能领域中的方向关系推理提供一个新的思路。     

连续值命题逻辑系统中公式的条件概率真度

基于条件概率的思想,在连续值命题逻辑系统中引入赋值密度函数概念,给出了公式的概率真度、数学期望、条件概率真度的定义,并得到了一些概率真度的推理规则。证明了Lukasiewicz逻辑系统中概率真度、条件概率真度在[0,1]中稠密。     

集合代数是经典命题演算形式系统的语义解释

经典命题演算形式系统(CPC)中的公式只是一些形式符号,其意义是由具体的解释给出的.逻辑代数和集合代数都是布尔代数,都是CPC的解释.集合代数是CPC的集合语义,其中对联结词的解释就是集合运算;对形式公式的解释就是集合函数;对逻辑蕴涵.逻辑等价的解释就是集合包含和集合相等=.标准概率逻辑是在标准概率空间上建立的逻辑体系,命题表示随机事件,随机事件是集合,概率空间中的事件域是集合代数,概率逻辑就是CPC集合语义的实际应用.CPC完全适用于概率命题演算.     

A Logical Formulation of Probabilistic Spatial Databases

There are numerous applications where there: is uncertainty over space and time. Examples of such uncertainty arise in vehicle tracking systems where we are not always sure where a vehicle is now (or may be in the future), and cell and satellite phone applications where we are not sure exactly where a phone may be, and so on. In this paper, we propose the concept of a Spatial Probabilistic Temporal (SPOT) database that contains statements of the form "Object O is in spatial region R at some time / with some probability in the interval [L, U]." We define the syntax and a declarative semantics for SPOT databases based on a mix of logic and linear programming, as well as query algebra. We show alternative implementations of some of these query algebra operators when the SPOT database has a disjoint/less property. Though the declarative semantics of SPOT databases is rooted in linear programming, we have found very efficient algorithms that do not use linear programming methods. We report on experiments we have conducted that show that the system scales to large numbers of SPOT atoms, as well as to fairly fine temporal and spatial granularity.     

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.     

An inquiry into computer understanding

This essay addresses a number of issues centered around the question of what is the best method for representing and reasoning about common sense (sometimes called plausible inference). Drew McDermott has shown that a direct translation of commonsense reasoning into logical form leads to insurmountable difficulties, from which McDermott concluded that we must resort to procedural ad hocery. This paper shows that the difficulties McDermott described are a result of insisting on using logic as the language of commonsense reasoning. If, instead, (Bayesian) probability is used, none of the technical difficulties found in using logic arise. For example, in probability, the problem of referential opacity cannot occur and nonmonotonic logics (which McDermott showed don't work anyway) are not necessary. The difficulties in applying logic to the real world are shown to arise from the limitations of truth semantics built into logic–probability substitutes the more reasonable notion of belief. In Bayesian inference, many pieces of evidence are combined to get an overall measure of belief in a proposition. This is much closer to commonsense patterns of thought than long chains of logical inference to the true conclusions. Also it is shown that English expressions of the “IF A THEN B” form are best interpreted as conditional probabilities rather than universally quantified expressions. Bayesian inference is applied to a simple example of linguistic information to illustrate the potential of this type of inference for AI. This example also shows how to deal with vague information, which has so far been the province of fuzzy logic. It is further shown that Bayesian inference gives a theoretical basis for inductive inference that is borne out in practice. Instead of insisting that probability is the best language for commonsense reasoning, a major point of this essay is to show that real inference is a complex interaction between probability, logic, and other formal representation and reasoning systems.     

概率图模型研究进展综述

概率图模型作为一类有力的工具,能够简洁地表示复杂的概率分布,有效地(近似)计算边缘分布和条件分布,方便地学习概率模型中的参数和超参数.因此,它作为一种处理不确定性的形式化方法,被广泛应用于需要进行自动的概率推理的场合,例如计算机视觉、自然语言处理.回顾了有关概率图模型的表示、推理和学习的基本概念和主要结果,并详细介绍了这些方法在两种重要的概率模型中的应用.还回顾了在加速经典近似推理算法方面的新进展.最后讨论了相关方向的研究前景.     

ON THE SEMANTICS OF STABLE INHERITANCE REASONING

Inheritance reasoners have traditionally been viewed as argument systems, or algorithms that determine reasonable conclusions by constructing acceptable arguments. While the intended meaning of links in such networks is understood, formal semantic accounts are troublesome, as are semantic accounts of the inference process. We adopt a different perspective, suggesting that links be interpreted as conditional sentences with appropriate truth conditions rather than uninterpreted "reasons." The conditional logic CT4D is used for this purpose. Furthermore, we characterize inference in our networks in terms of preferred (or minimal) models. In the process, we identify some key differences between our account of inference and those based on the notion of inferential distance, specifically with respect to the stability of reasoning. Key words: nonmonotonic reasoning, inheritance hierarchies, minimal models, conditional logic.