面向功能的表达系统HAR

HAR(Hypothesis Associative Representation)系统是一个面向功能的表达系统,由三部分组成:表达语言HARL、知识库管理子系统和推理机.HAR系统提供了三级描述和假设联想功能,把领域知识的表达和运用知识的推理控制策略紧密地结合在一起,实现对子问题求解的功能化表达.     

粗糙集理论在故障诊断规则获取中的应用

本文的目的是给出一种利用粗糙集理论解决故障诊断的规则获取问题的方法 ,该方法的特点是可以处理由于类重叠引起的样本信息不精确、不一致情况下的规则获取 .以规则形式表示的知识接近于人脑推理过程 ,因此基于规则的诊断方法在故障诊断中得到广泛使用 ,但规则获取是其瓶颈之一 .粗糙集 (RS)理论是为开发自动规则生成系统而提出的 ,其主要思想是在保持分类能力不变的前提下 ,通过知识约简 ,导出概念的分类规则 .因此 ,可以把 RS理论用于规则的故障诊断中 .本文给出了基于决策矩阵和决策函数的获取规则方法的流程图 ,以故障诊断实例说明其使用方法 ,并验证了其有效性     

模糊联想推理及其实现

本文给出了用模糊联想记忆网络实现肯定前件式,否定后件式或同时包含这两种模糊推理形式的充要条件,并提出了一个增加网络神经元的增强学习算法,这种学习算法能够可靠有效地用于任意多个基于规则的不同形式模糊推理。     

一种用于基于知识系统开发的形式化模型*

LKo是一个将面向对象和逻辑范型相结合，用于基于知识系统的形式化开发模型，其中逻辑对象是集状态、约束、行为、继承于一体的抽象实体．它支持框架、规则、语义网络、黑板等多种知识表示，因而可用来形式地描述基于知识系统的需求规范．在知识获取过程中通过对形式规范反复地修改、验证及确认而形成软件原型．     

基于连通结构与动力学过程的知觉记忆层次模型

根据认知的计算神经科学的观点,提出了一种基于神经系统动力学理论和连通图的信息的直接表达方式.它首先定义了知觉信息直接表达的神经结构和动力学模式,然后提出一个双层的网络计算模型,分别用于记录外界刺激的特征信息和连通对应的特定神经回路的连接模式,这是通过结构学习来实现的.在两层神经元间建立起来的连通结构同时起到联想记忆的作用,记忆的可靠程度由神经回路的连通度来决定.这种直接表达方式对于人工智能中关于语义表达和基于语义的推理研究具有重要意义.     

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.     

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。     

基于神经网络的交互式自动获取知识的N－R方法

本文为解决知识系统构造过程中的瓶颈问题──知识获取，提出了一种基于神经网络ＮＮ（ｎｅｕｒａｌｎｅｔｗｏｒｋ）的自动获取多级推理产生式规则的Ｎ－Ｒ方法，该方法采用了特有的ＮＮ结构模型和相应的学习算法，使得ＮＮ在学习过程中动态确定隐层节点数的同时，也产生了样例集中没有定义的新概念，学习后的ＮＮ能用本文提出的转换算法转换成推理网络，最终方便地得到产生式规则集.     

基于模糊神经网络结构的多值联想记忆

文章基于模糊神经网络结构,即通过模糊化,推理,去模糊三个过程,把Kosko提出的模糊联想记忆(FAM)网络模型应用到容错性需要较强的多值联想记忆中,解决了这种网络模型不能对随机噪声模式正确联想的问题,新的网络模型设计简单,大量实验表明文中的联想记忆网络大大提高了FAM网络的容错性能。     

基于知识图谱的在线商品问答研究

现阶段,针对商品的自动问答主要由意图识别和答案配置来实现,但问题答案的配置依赖人工且工作量巨大,容易造成答案质量不高。随着知识图谱技术的出现和发展,基于知识图谱的自动问答逐渐成为研究热点。目前,基于知识图谱的商品自动问答主要是通过规则解析的方法将文本形式问题解析为知识图谱查询语句来实现。虽然减少了人工配置工作,但其问答效果受限于规则的质量和数量,很难达到理想的效果。针对上述问题,该文提出一种基于知识图谱和规则推理的在线商品自动问答系统。主要贡献包括: ①构建一个基于LSTM的属性注意力网络SiameseATT(Siamese attention network)用于属性选择; ②引入了本体推理规则,通过规则推理使得知识图谱能动态生成大量三元组,使得同样数据下可以回答更多问题。在NIPCC-ICCPOL 2016 KBQA数据集上的实验显示,该系统具有很好的性能。相比一些更复杂的模型,该问答系统更适合电商的应用场景。     

Belief rule-base inference methodology using the evidential reasoning Approach-RIMER

In this paper, a generic rule-base inference methodology using the evidential reasoning (RIMER) approach is proposed. Existing knowledge-base structures are first examined, and knowledge representation schemes under uncertainty are then briefly analyzed. Based on this analysis, a new knowledge representation scheme in a rule base is proposed using a belief structure. In this scheme, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness, and nonlinear causal relationships, while traditional if-then rules can be represented as a special case. Other knowledge representation parameters such as the weights of both attributes and rules are also investigated in the scheme. In an established rule base, an input to an antecedent attribute is transformed into a belief distribution. Subsequently, inference in such a rule base is implemented using the evidential reasoning (ER) approach. The scheme is further extended to inference in hierarchical rule bases. A numerical study is provided to illustrate the potential applications of the proposed methodology.     

A new belief rule base knowledge representation scheme and inference methodology using the evidential reasoning rule for evidence combination

In this paper, we extend the original belief rule-base inference methodology using the evidential reasoning approach by i) introducing generalised belief rules as knowledge representation scheme, and ii) using the evidential reasoning rule for evidence combination in the rule-base inference methodology instead of the evidential reasoning approach. The result is a new rule-base inference methodology which is able to handle a combination of various types of uncertainty.Generalised belief rules are an extension of traditional rules where each consequent of a generalised belief rule is a belief distribution defined on the power set of propositions, or possible outcomes, that are assumed to be collectively exhaustive and mutually exclusive. This novel extension allows any combination of certain, uncertain, interval, partial or incomplete judgements to be represented as rule-based knowledge. It is shown that traditional IF-THEN rules, probabilistic IF-THEN rules, and interval rules are all special cases of the new generalised belief rules.The rule-base inference methodology has been updated to enable inference within generalised belief rule bases. The evidential reasoning rule for evidence combination is used for the aggregation of belief distributions of rule consequents.     

Categorial expert systems

Abstract: Expert systems can be used to determine some objects or consequences from uncertain knowledge by hierarchical categorization. Categorical representation is psychologically motivated and also offers an explanation of how to deal with uncertain knowledge based on counting during approximate reasoning. It is an alternative to other well‐known uncertainty calculi. A knowledge base which is used during approximate reasoning is represented by a taxonomical arrangement of verbal categories. Priming eases the formation of the final hypothesis, as more exact possible hypotheses are formed. The approximate reasoning is demonstrated on an expert system ‘Jurassic’ from the field of paleontology for the determination of a dinosaur species. It helps the paleontologist to determine creatures from uncertain knowledge. The system is composed of 423 rules arranged in a directed acyclic graph with a depth of 5. This knowledge is represented by a taxonomical arrangement of verbal categories represented by associative memories.     

Associative diagnosis

Abstract: We present a new architecture of a diagnostic system composed of an associative memory with feedback connections. This new architecture requires limited computer resources, it is fast, and it can run on small computers. The diagnostic process is described by a deduction system that performs an abductive inference. The abductive inference itself is explained by the verbal category theory. We model both processes by an associative memory that performs the inference with the aid of the feedback connections. The represented knowledge is arranged in groups that define taxonomy. An embedded diagnostic system for the determination of a disorder with applications in modern industrial machines is presented.     

基于知识表示学习的实时语义数据流推理

传统的语义数据流推理使用前向或后向链式推理产生确定性的答案,但是在复杂的传递规则推理中效率不高,无法满足实时数据流处理场景对答案的及时性要求。因此,提出一种基于联合嵌入模型的知识表示方法,并应用于语义数据流处理中。将规则与事实三元组联合嵌入并利用深度学习模型进行训练,在推理阶段,根据查询中涉及的规则建立推理模板,利用深度学习模型对推理模板产生的三元组进行预测和分类,将结果作为查询和推理答案输出。实验表明,对于复杂规则推理,基于知识表示学习的实时语义数据流推理能够在保障较好推理准确性和命中率的前提下有效地降低延迟。     

Logical form generation as abduction: Part I. Representation of linguistic concepts

For some time, researchers have become increasingly aware that some aspects of natural language processing can be viewed as abductive inference. This article describes knowledge representation in dual-route parsimonious covering theory, based on an existing diagnostic abductive inference model, extended to address issues specific to logic form generation. the two routes of covering deal with syntactic and semantic aspects of language, and are integrated by attributing both syntactic and semantic facets to each “open class” concept. Such extensions reflect some fundamental differences between the two task domains. the syntactic aspect of covering is described to show the differences, and some interesting properties are established. the semantic associations are characterized in terms of how they can be used in an abductive model. A major significance of this work is that it paves the way for a nondeductive inference method for word sense disambiguation and logical form generation, exploiting the associative linguistic knowledge. This approach sharply contrasts with others, where knowledge has usually been laboriously encoded into pattern-action rules that are hard to modify. Further, this work represents yet another application for the general principle of parsimonious covering. © 1994 John Wiley & Sons, Inc.     

知识进化算法及其在关联分类中的应用

针对传统的关联分类算法在构造分类器的过程中需要多次遍历数据集从而消耗大量的计算、存储资源的问题,该文提出了一种基于知识进化算法的分类规则构造方法。该方法首先对数据集中的数据进行编码;然后利用猜测与反驳算子从编码后的数据中提取出猜测知识和反面知识;接着对提取出来的猜测知识进行覆盖度、正确度的计算,并根据不断变化的统计数据利用萃取算子将猜测知识与反面知识进行合理的转换。当得到的知识集中的知识的覆盖度达到预设的阈值时,该数据集中的知识被用来生成分类器进行分类。该方法分块读入待分类的数据集,极大地减少了遍历数据集的次数,明显减少了系统所需的存储空间,提高了分类器的构造效率。实验结果表明,该方法可行、有效,在保证分类精度的前提下,较好地解决了关联分类器构造低效、费时的问题。     

Parallel inference in dynamic decision support systems of a semiotic type

The organization of parallel inference in dynamic decision support systems (DDSS) of a semiotic type, oriented towards a solving of ill-formed problems in dynamic applied domains, is considered. As a knowledge representation model, there are used production rules reflecting expert knowledge about a problem domain, an environment and decision making processes. The main concepts and assertions defining possibility and impossibility of parallel executing the production rules are given. Several types of parallelism in an inference process are introduced. The corresponding algorithm of parallel inference is described. Thus, the purpose of this paper is to develop and to research parallel inference methods and procedures that provide efficient processing a large amount of production rules for DDSS of a semiotic type.     

一种基于交互式知识发现的入侵事件关联方法研究

分析了入侵检测系统的现存问题，总结了入侵事件关联系统的最新进展和缺陷，提出一个基于人机交互式知识发现的入侵事件关联系统．该系统离线部分在入侵事件关联领域首次引入FP-Tree和WINEPI算法进行交互式知识发现，并将发现的频繁模式和序列模式转化成人侵事件关联规则；在线部分利用先验知识和交互式知识发现的关联知识，以嵌入式CLIPS推理组件作为推理引擎，对多个入侵检测器上报的事件进行高效关联和归并．在集成化网络安全监控及防卫系统Net-Keeper中的实际应用表明本系统是一个开放、高效的入侵事件关联平台．