不确定性推理中确定性的传播

系统行为的确定性在不确定性推理方法中的合理传播是每一种不确定性推理方法均需考虑的问题,系统行为的确定性是控制和决策的一个重要参考信息,也是衡量一种不确定性推理方法发展成熟与否的一个标志.主观Bayes方法、确定性理论和灰色定性仿真是三种考虑了确定性在推理过程中传播,具有代表性的不确定性推理方法,它们的确定性传播方法被介绍,并对其优缺点进行了分析,结果显示灰色定性仿真的传播方法更为合理.     

证据理论在不确定性推理中的应用研究*

利用证据理论中的基本概率分配函数、信任函数和似然函数来描述和处理知识的不确定性。提出一个特殊的概率分配函数和新的组合规则,并以其为基础建立一个不确定性推理模型。实例证明该模型能有效地度量最终结论的不确定性。     

青铜器鉴定专家系统中的不确定性推理

讨论了青铜器鉴定专家系统的不确定性推理方法,包括知识的不精确表示,推理规则的不精确表示,组合证据的不精确描述以及推理规则的更新。并将该推理方法应用干文物鉴定领域,成功建立了青铜器鉴定专家系统。     

不确定性推理的实用性研究

Bayes 概率推理是不确定性推理方法之一,它在理论上虽然比较简单,但当证据之间不满足独立性假定时,推理过程中需要专家给出的先验概率个数将按几何级数增加,这严重影响了 Bayes 概率推理的实用性.本文提出的两种处理非独立性问题的概率推理方法,不仅可以大大减少专家的工作量,而且比较简单、直观,便于实际应用.     

证据推理理论及其应用

证据理论既能够灵活处理不确定信息, 包括随机性、模糊性、不准确性和不一致性, 又能够有效融合定量信息和定性知识. 目前, 证据理论已广泛应用于评估与决策等多个领域中, 包括多属性决策分析、信息融合、模式识别和专家系统等. 本文从D-S证据理论出发, 针对Dempster组合规则存在的“反直觉”问题和组合爆炸, 主要围绕置信分布理论系统地梳理了证据理论的发展过程, 总结分析了国内外典型文献, 最后从实际应用对证据理论进行了简要的评述和展望.     

粗糙集理论中不确定性的粗糙信息熵表示

1 引言粗糙集理论从新的视角对知识进行了定义,把知识看作是关于论域的划分,认为知识是具有粒度的(granularity),即知识是粗糙的。知识的粒度越大,其越粗糙,知识含量就越少。并认为知识的不确定性是因知识粒度太大引起的,知识的粗糙性越大,则其不确定性也越大。在粗糙集理论中,一个集合由其上逼近集合和下逼近集合来近似,因此集合存在着不确定性。另一方面,在信息论中,信息熵的概念从物理概念上反映了知识库的知识含量、不确定性及随机性的本质。因此,本文从信息熵的角度更深刻地反映知识的不确定性及集合的不确定性。     

基于不确定性推理的活动识别方法研究

活动识别已成为智能家居领域的研究热点,目前国内外有关活动识别方法的研究有很多,研究人员提出了不同的方法来进行活动建模和识别,可分为数据驱动方法和知识驱动方法.数据驱动方法容易受到维数的限制,并且需要大量的数据集来训练出活动模型.目前在有关活动识别研究的方法中缺少一种既能够考虑到异构数据之间的知识共享,又能够考虑到活动的...     

视觉导引可移动机器人中的空间不确定性的表示和推理

本文通过对视觉导引可移动机器人建模、导航和规划中涉及的空间不确定性问题的研究,提出了一套系统的空间不确定性的表示和推理方法.     

基于贝叶斯网络的本体不确定性推理

运用OWL语言扩展了本体对领域知识的不确定性表示,并基于贝叶斯网络实现了本体领域知识的不确定性推理。实验表明将贝叶斯网络与本体结合起来,能够充分发挥本体在知识描述方面的优势和贝叶斯网络的推理能力,实现依据部分信息的概率描述获取知识,指导实践。     

A hybrid approach to deductive uncertain inference

Deductive uncertain inference has been one of the most important ways of handling uncertainty. In this paper we report the development of a hybrid approach to such an inference. This approach has been implemented in a system which is based on INFERNO but integrates the strength of probabilisitc logic and Dempster's rule.     

Robust fusion of uncertain information.

A technique is presented to combine n data points, each available with point-dependent uncertainty, when only a subset of these points come from N < n sources, where N is unknown. We detect the significant modes of the underlying multivariate probability distribution using a generalization of the nonparametric mean shift procedure. The number of detected modes automatically defines N, while the belonging of a point to the basin of attraction of a mode provides the fusion rule. The robust data fusion algorithm was successfully applied to two computer vision problems: estimating the multiple affine transformations, and range image segmentation.     

A mathematical formulation of uncertain information

This paper introduces a mathematical model of uncertain information. Each body of uncertain information is an information quadruplet, consisting of a code space, a message space, an interpretation function, and an evidence space. Each information quadruplet contains prior information as well as possible new evidence which may appear later. The definitions of basic probability and belief function are based on the prior information. Given new evidence, Bayes' rule is used to update the prior information. This paper also introduces an idea of independent information and its combination. A combination formula is derived for combining independent information. Both the conventional Bayesian approach and Dempster-Shafer's approach belong to this mathematical model. A Bayesian prior probability measure is the prior information of a special information quadruplet; Bayesian conditioning is the combination of special independent information. A Dempster's belief function is the belief function of a different information quadruplet; the Dempster combination rule is the combination rule of independent quadruplets. This paper is a mathematical study of handling uncertainty and shows that both the conventional Bayesian approach and Dempster-Shafer's approach originate from the same mathematical theory.This work was supported in part by the National Science Foundation under grant number IRI-8505735 and a summer research grant of Ball State University.     

Aggregation of imprecise and uncertain information in databases

Information stored in a database is often subject to uncertainty and imprecision. Probability theory provides a well-known and well understood way of representing uncertainty and may thus be used to provide a mechanism for storing uncertain information in a database. We consider the problem of aggregation using an imprecise probability data model that allows us to represent imprecision by partial probabilities and uncertainty using probability distributions. Most work to date has concentrated on providing functionality for extending the relational algebra with a view to executing traditional queries on uncertain or imprecise data. However, for imprecise and uncertain data, we often require aggregation operators that provide information on patterns in the data. Thus, while traditional query processing is tuple-driven, processing of uncertain data is often attribute-driven where we use aggregation operators to discover attribute properties. The aggregation operator that we define uses the Kullback-Leibler information divergence between the aggregated probability distribution and the individual tuple values to provide a probability distribution for the domain values of an attribute or group of attributes. The provision of such aggregation operators is a central requirement in furnishing a database with the capability to perform the operations necessary for knowledge discovery in databases     

Some notes on fusion of uncertain information

This article attempts to analyze the combination of uncertain pieces of information, particularly, given several pieces of information, each of which is assigned a certainty factor. The problem then is whether it is possible to find the certainty factor associated with the combination result. This problem, even if it has been widely commented on in probability literature from various viewpoints, sounds less analyzed in the framework of possibility or evidence theories. This study investigates various proposals for quantifying certainty qualification and then constructs the certainty assigned for the combination result of initial inputs. First, we shall consider some basic combination modes and then attempt to generalize the result for more general combination modes. © 2004 Wiley Periodicals, Inc.     

Measures of assurance and opportunity in modeling uncertain information

Our focus is on the representation of uncertain information using set measures. We first discuss the basic properties of monotonic set measures. We then discuss the appropriateness of their use in modeling uncertain information. We look at some notable types of measures of uncertain information and investigate in considerable detail cardinality‐based measures. We look at the Sugeno measure and provide a formulation of the underlying cardinality‐based measures. We then look at quasi‐additive uncertainty measures. We discuss the entropy and attitudinal character of an uncertainty measure. Finally, we introduce the ideas of the assurance and opportunity of the occurrence of an outcome. © 2012 Wiley Periodicals, Inc.     

Discrete time control problems with uncertain information

Discrete-time control problems described by a state vectorx _i , a control vectoru _i , and a parameter vectorm _i are considered. Using an information spectrum, we define what is known and how to model uncertainties in our system. Depending on the information available to the control unit of the system, we consider feasibility, stability, optimality, and learning ability of such systems with incomplete information. For the special case of a deterministic, memoryless state-set feedback control law, we develop a criterion for global uniform asymptotic stability about the zero state and a dynamic programming approach to optimize processes with a finite time horizon. A technique for solving the generalized Bellman functional equations is outlined.     

不确定时态信息表示的统一模型

时态信息表示和推理是人工智能研究中的一个重要课题,现有的模型大多只能表示确定时态信息,然而现实生活中很多事件的发生结束等时态信息都是不确定的。故提出了一个表示不确定时态信息的统一模型,可用于描述各种具有确定或不确定时态信息的事件。该模型首先定义各类时态对象(如时间点、时间区间)以及它们之间的关系,并给出时态对象间的传递关系表,利用该表能进行时态一致性约束满足问题的求解。最后,给出了两个不确定时态推理的例子,表明了该模型的实际应用意义。     

An approximate inference with Gaussian process to latent functions from uncertain data

Most formulations of supervised learning are often based on the assumption that only the outputs data are uncertain. However, this assumption might be too strong for some learning tasks. This paper investigates the use of Gaussian processes to infer latent functions from a set of uncertain input-output examples. By assuming Gaussian distributions with known variances over the inputs-outputs and using the expectation of the covariance function, it is possible to analytically compute the expected covariance matrix of the data to obtain a posterior distribution over functions. The method is evaluated on a synthetic problem and on a more realistic one, which consist in learning the dynamics of a cart-pole balancing task. The results indicate an improvement of the mean squared error and the likelihood of the posterior Gaussian process when the data uncertainty is significant.