根据观察获取知识

本文介绍一种建立知识库的学习方法。从人类专家吸取知识的知识获取系统有两种类型:交互式和非交互式的。本文介绍非交互式知识获取系统,它依据观察从人类专家获得知识。这种知识获取系统学习人类专家用以解决问题的策略和从瞬时序列数据中抽取逻辑规则,其学习方法是基于翻译的学习(IBL),在学习过程中要用到预备知识。IBL 有两个子系统:翻译系统和学习系统。前者负责把现实世界的信息翻译成内部规则形式,而规则维护系统负责生成和界定知识。本文将介绍翻译系统和规则生成的预处理过程。     

一种改进的规则知识获取方法

知识获取是建立专家系统的最基本最重要的过程,但它又是研制和开发专家系统的“瓶颈”。文章提出了一种改进的规则知识机器自动获取技术,它将学习看作是在一个符号描述空间中的启发式搜索过程,能够通过归纳从专家决策的例子中确定决策规则,从而大大简化了从专家到机器的知识转换过程。     

计算机硬件维修知识库的知识表示和知识获取

本文以计算机硬件售后的维修服务为模型,为计算机硬件出现的故障及对应解决方案创建知识库.硬件故障知识采用事例表示法和产生式规则表示法两种方法,并将知识分为事例知识和规则知识两类.在事例知识的获取方面采用了自动获取和人工干预两种形式,在规则知识的获取方面采用的是人工干预形式.     

一种基于粗糙集的诊断型知识发现方法

知识获取是开发专家系统的瓶颈，传统的病害知识获取通常需要一个较长的过程．针对这一问题，本文给出了粗糙集理论和基于规则的作物病害知识之间的关系，即在油菜植病专家知识获取过程中的应用，建立了基于RHINOS 诊断模型的油菜病害的排除规则、包含规则和病害映像，阐述了可能性规则定义，并给出了基于规则描述的自动归纳推理方法，包括全搜索过程、后处理过程、统计测试的评估以及交叉验证和鞋带方法等．实验结果表明，粗糙集不仅是一个很好的知识获取的框架，而且能正确的归纳推理植物病害的规则．这对诊断型专家系统的开发可起到一个很好的辅助作用，在智能化农业信息系统中有着广泛的应用前景．     

分类判断策略的自动获取

获取专家在决策、问题求解过程中使用的决策策略和问题求解规则是知识获取的核心目标．本文以“ｔｈｅＭｏｖｉｎｇＢａｓｉｓＨｅｕｒｉｓｔｉｃｓ”模型为背景，介绍了自动获取专家在分类判断任务中运用的判断策略的算法思想与系统Ｐｏｌｙｎｏｍｅ的组成．     

网络数据智能提取

HTML是当前编写网页使用的最多的语言，如何利用HTML，这种标记性语言的特点，让计算机提供给用户快速获取知识的途径成为许多计算机工作者的一大难题。该文介绍一个有效的从互联网上提取知识的方法供参考。把这种方法简单的分成4个途径。①规范化基于HTML的网页脚本；②将网页结构和内容用一棵内容树表示；③比较相似网页的内容树生成提取规则；④将规则和下载程序结合得到最终的知识获取软件。     

基于场景及规则获取模板的知识获取方法

产生式规则作为知识库系统进行推理的常用的、可读性好的知识表示形式,在构建知识库系统时有极大的优越性.提出一种基于场景及规则获取模板的知识获取方法,并以某高分子复合材料的加工专家为知识获取对象.该方法通过分析、记录领域专家进行设计的过程、解决问题的过程和动作,将领域问题按层次细化为一系列子问题,并在子问题场景下结合场景模型及知识获取模板来获取规则性知识.采用该方法可以辅助领域专家在明晰领域知识结构的基础上,逐步挖掘领域中细粒度的规则性知识.     

KAL:一个可学习的知识获取环境

可学习的知识获取环境KAL帮助软件工程师从领域专家那儿获取专家知识,形成一个 具有继承性的概念层次结构.该环境为知识获取提供视图(viewing)、图示(graphing)、概 念聚类和概念发现等工具.     

模型管理系统中构模知识的组织和获取方法

提出了一种构模知识的组织和获取方法。构模知识分为模型知识、领域知识和数据知识3个层次，数据知识是最底层的知识，高层知识的应用通过调用底层知识完成。模型知识和领域知识可以通过交互的方式在专家的帮助下获取，数据知识可以由系统自动获取。应用这种方法，系统能够在专家构建模型时获取构模的知识，从而使模型资源得到有效利用，并解决了模型管理系统知识获取的瓶颈问题。     

知识获取研究

知识获取是知识从外部知识源到计算机内部的转换过程,是当前知识工程研究的热点和难点问题之一。该文阐述了知识获取的定义、方法和最终目标,重点介绍了显性知识和隐性知识的获取方法,并分析了这两类方法的优缺点,最后给出了知识获取的困难和一些改进思想。     

A qualitative discriminant approach for generating quantitativebelief functions

The authors present an approach that will be useful in knowledge acquisition from experts on the degree of belief in, or the probability of, the truthfulness of various propositions. Its advantages include exploring the given problem situation using linguistic quantifiers; avoiding the premature use of numeric measures; and identifying input data that is inconsistent with the theory of belief functions     

知识云及其在知识获取中的意义

如何有效地获取领域专家的知识一直被视为人工智能中的难题．本文的目的在于提出一种关于知识获取的基本观点，并以此观点为基础来研究知识获取问题．这个观点的基本内容是：象波尔的原于模型一样，在专家的知识周围有一些知识层．它们是认识和获取专家知识的突破口或入口．本文将这些知识层看成是知识云，同时将上述观点视作知识云假设．我们将讨论知识云的内涵，研究知识云假设的合理性，最后将阐述知识云这一概念在知识获取中的意义．     

A Thorough Axiomatization of a Principle of Conditional Preservation in Belief Revision

Although the crucial role of if-then-conditionals for the dynamics of knowledge has been known for several decades, they do not seem to fit well in the framework of classical belief revision theory. In particular, the propositional paradigm of minimal change guiding the AGM-postulates of belief revision proved to be inadequate for preserving conditional beliefs under revision. In this paper, we present a thorough axiomatization of a principle of conditional preservation in a very general framework, considering the revision of epistemic states by sets of conditionals. This axiomatization is based on a nonstandard approach to conditionals, which focuses on their dynamic aspects, and uses the newly introduced notion of conditional valuation functions as representations of epistemic states. In this way, probabilistic revision as well as possibilistic revision and the revision of ranking functions can all be dealt with within one framework. Moreover, we show that our approach can also be applied in a merely qualitative environment, extending AGM-style revision to properly handling conditional beliefs.     

Self-Conditional Probabilities and Probabilistic Interpretations of Belief Functions

We present an interpretation of belief functions within a pure probabilistic framework, namely as normalized self-conditional expected probabilities, and study their mathematical properties. Interpretations of belief functions appeal to partial knowledge. The self-conditional interpretation does this within the traditional probabilistic framework by considering surplus belief in an event emerging from a future observation, conditional on the event occurring. Dempster's original interpretation, in contrast, involves partial knowledge of a belief state. The modal interpretation, currently gaining popularity, models the probability of a proposition being believed (or proved, or known). The versatility of the belief function formalism is demonstrated by the fact that it accommodates very different intuitions.     

From the knowledge acquisition bottleneck to the knowledge acquisition overflow: A brief French history of knowledge acquisition

This article is an account of the evolution of the French-speaking research community on knowledge acquisition and knowledge modelling echoing the complex and cross-disciplinary trajectory of the field. In particular, it reports the most significant steps in the parallel evolution of the web and the knowledge acquisition paradigm, which finally converged with the project of a semantic web. As a consequence of the huge amount of available data in the web, a paradigm shift occurred in the domain, from knowledge-intensive problem solving to large-scale data acquisition and management. We also pay a tribute to Rose Dieng, one of the pioneers of this research community.     

Knowledge-based temporal interpolation

Temporal interpolation is the task of bridging gaps between time-oriented concepts in a context-sensitive manner. It is a subtask important for solving the temporal-abstraction task-abstraction of interval-based, higher-level concepts from time-stamped data. We present a knowledge-based approach to the temporal-interpolation task and discuss in detail the precise knowledge required by that approach, its theoretical foundations, and the implications of the approach. The temporal-interpolation computational mechanism we discuss relies, among other knowledge types, on a temporal-persistence model. The temporal-persistence model employs local temporal-persistence functions that are temporally bidirectional (i.e. extend a belief measure in a predicate both into the future and into the past) and global, maximal-gap temporal-persistence functions that bridge gaps between interval-based predicates. We investigate the quantitative and qualitative properties implied by both types of persistence functions. We have implemented our approach in the RÉSUMÉ program and evaluated it in several different medical and engineering domains. We discuss the implications of our conceptual and computational methodology for acquisition, maintenance, reuse, and sharing of temporal-abstraction knowledge.     

基于范式转换的知识进化算法

根据库恩的知识进化观,提出一种基于范式转换的知识进化算法。每个范式对应一个问题的可行解,以范式为单位建立初始知识库。利用传承算子实现对优秀范式的传承,采用修补算子实现范式危机的消除,以创新算子产生新范式,从知识库的最优范式中获取问题的最优解。将该算法应用于求解函数极小值,其结果与遗传算法相比具有更好的寻优性能。     

无限论域中的粗糙近似空间与信任结构

在粗糙集理论中存在一对近似算子:下近似算子和上近似算子.而在Dempser-Shafer证据理论中有一对对偶的不确定性测度:信任函数与似然函数.集合的下近似和上近似可以看成是对该集合所表示信息的定性描述,而同一集合的信任测度和似然测度可以看成是对该集合的不确定性的定量刻画.针对各种复杂系统中不确定性知识的表示问题,介绍了无限论域中经典和模糊环境下信任结构及其导出的信任函数与似然函数的概念,建立了Dempser-Shafer证据理论中信任函数与似然函数和粗糙集理论中下近似与上近似之间的关系.阐述了由近似空间导出的下近似和上近似的概率生成一对对偶的信任函数和似然函数;反之,对于任何一个信任结构及其生成的信任函数与似然函数,必可以找到一个概率近似空间,使得由近似空间导出的下近似和上近似的概率分别恰好就是所给的信任函数和似然函数.最后,指出了主要理论成果在智能信息系统的知识表示和知识获取方面的潜在应用.     

On the dempster-shafer framework and new combination rules

We discuss the basic concepts of the Dempster-Shafer approach, basic probability assignments, belief functions, and probability functions. We discuss how to represent various types of knowledge in this framework. We discuss measures of entropy and specificity for belief structures. We discuss the combination and extension of belief structures. We introduce some concerns associated with the Dempster rule of combination inherent in the normalization due to conflict. We introduce two alternative techniques for combining belief structures. The first uses Dempster's rule, while the second is based upon a modification of this rule. We discuss the issue of credibility of a witness.     

An historical overview of natural language processing systems that learn

A fundamental issue in natural language processing is the prerequisite of an enormous quantity of preprogrammed knowledge concerning both the language and the domain under examination. Manual acquisition of this knowledge is tedious and error prone. Development of an automated acquisition process would prove invaluable.This paper references and overviews a range of the systems that have been developed in the domain of machine learning and natural language processing. Each system is categorised into either a symbolic or connectionist paradigm, and has its own characteristics and limitations described.