基于Petri网的知识库维护方法的研究

Ｐｅｔｒｉ网作为建立系统模型，性能分析，模拟，系统调度，控制等方面的工具已在计算机各方面得到了很好的应用。本文主要探讨了Ｐｅｔｒｉ网在知识库维护方面的应用，包括知识库的正确性、一致性及完备性的检查。对大型知识库的建立及其维护具有重要意义。     

基于知识库的PKI等级评估模型的研究

提出了一种基于知识库的PKI等级评估模型.目前国内外对于PKI等级评估的研究大多集中于定性评估,并且依赖于评估者的专业水平.为了能够使PKI的等级评估方便而精确,借鉴了一种著名的风险评估工具的思想,即基于专家系统知识库的评估体系.在此基础上,结合PKI等级评估的自身特性,提出一套由评估依据产生的规则库,并在规则库之上给出了以事实表、前件表、后件表三表组成的知识库模型和由表中相关键值驱动的推理演绎算法,从而得出一个自成体系的PKI等级评估工具的模型.     

Expert guided integration of induced knowledge into a fuzzy knowledge base

This paper proposes a method for building accurate and interpretable systems by integrating expert and induced knowledge into a single knowledge base. To favor the cooperation between expert knowledge and data, the induction process is run under severe constraints to ensure the fully control of the expert. The procedure is made up of two hierarchical steps. Firstly, a common fuzzy input space is designed according to both the data and expert knowledge. The compatibility of the two types of partitions, expert and induced, is checked according to three criteria : range, granularity and semantic interpretation. Secondly, expert rules and induced rules are generated according to the previous common fuzzy input space. Then, induced and expert rules have to be merged into a new rule base. Thanks to the common universe resulting from the first step, rule comparison can be made at the linguistic level only. The possible conflict situations are managed and the most important rule base features, consistency, redundancy and completeness, are studied. The first step is thoroughly described in this paper, while the second is only introduced.     

为测试系统诊断设计并构造规则库和推理机

目前ＶＬＳＩ测试系统日趋复杂，要快速，精确地对它们进行诊断，不得不求助于专家系统，专家知识和解释专家知识并控制诊断进程的机制是专家系统的两大要素，它们又分别被称为规则库和推理机，针对这两大要素，本文阐述以下两方面的问题：（１）设计规则库的结构，（２）设计并构造用于ＶＬＳＩ测试系统诊断的推理机。     

Iterative learning belief rule-base inference methodology using evidential reasoning for delayed coking unit

The belief rule-base inference methodology using evidential reasoning (RIMER) approach has been proved to be an effective extension of traditional rule-based expert systems and a powerful tool for representing more complicated causal relationships using different types of information with uncertainties. With a predetermined structure of the initial belief rule-base (BRB), the RIMER approach requires the assignment of some system parameters including rule weights, attribute weights, and belief degrees using experts’ knowledge. Although some updating algorithms were proposed to solve this problem, it is still difficult to find an optimal compact BRB. In this paper, a novel updating algorithm is proposed based on iterative learning strategy for delayed coking unit (DCU), which contains both continuous and discrete characteristics. Daily DCU operations under different conditions are modeled by a BRB, which is then updated using iterative learning methodology, based on a novel statistical utility for every belief rule. Compared with the other learning algorithms, our methodology can lead to a more optimal compact final BRB. With the help of this expert system, a feedforward compensation strategy is introduced to eliminate the disturbance caused by the drum-switching operations. The advantages of this approach are demonstrated on the UniSim? Operations Suite platform through the developed DCU operation expert system modeled and optimized from a real oil refinery.     

基于相似性度量的氧化铝配料知识库在线维护

针对烧结法氧化铝优化配料专家系统知识库的结构特点,提出了一种基于相似性度量的专家知识库在线维护方法。构造规则的相似性度量函数,以此为基础进行规则不一致性判断,并遵循原有的知识组织策略在线实现规则的有序添加和修改,从而保证高效的专家推理,提出的方法已成功用于工业应用。     

Rule effectiveness in rule-based systems: A credit scoring case study

Rule-based systems may sometimes grow very large, making their acceptance by users and their maintenance quite problematic. One therefore needs to make rule-bases as compact as possible. The classical definition of rule redundancy in the literature is based upon logic and graph theory. Another, complementary, view of redundancy is proposed here. The suggested approach is based on the contribution of individual rules to the overall system’s accuracy.

It is shown here, though an analysis of a real-world credit scoring rule-based system, that by taking into account system’s accuracy, one can sometimes significantly reduce the size of a rule-base; even one which is already free from logic-related abnormalities. The approach taken here is not proposed as a substitution to classical logic and graph-based methods. Rather, it complements them.     

一种基于Petri网思想的知识库的动态维护方法

本文研究了基于规则的专家系统知识库的维护问题，在添加一条新规则后，知识库可能出现矛盾规则，循环规则，冗余规则，从属规则等。文中详细给出了基于Ｐｅｔｒｉ网思想的对知识库进行维护的算法，进而决定是否添加该规则，从而保证了知识库的一致性，为专家系统进行正确推理，提高推理速度打下了坚实的基础。     

Minimizing maintenance anomalies in expert systems

Expert systems are emerging as a powerful technology for solving many problems previously requiring human experts. However, maintenance has been identified as a major difficulty in expert system implementations. Surprisingly, the problem of maintenance has only recently begun to receive attention in expert systems research, though it has long been an issue in databases. Databases are in a constant state of change, and the prevention of maintenance anomalies is essential. As similar maintenance operations are performed on rule bases, this paper investigates techniques to avoid maintenance anomalies in expert system rule bases. The result is an expert system rule base structure that is appropriate for volatile production use. In addition to lower maintenance demands, this approach favorably impacts on verification, computational efficiency, and storage requirements.     

The effect of knowledge representation schemes on maintainabilityof knowledge-based systems

Uses an experimental approach to investigate the representational effects of knowledge on maintainability, and compares this with the structural effects of rule sets investigated by Davis (1990). Results show that an object-based system, compared to a structured rule-based system, was easier to maintain in terms of the time to do the maintenance tasks, but not necessarily in terms of accuracy of the alterations. However, in some instances, subsumption and redundancies were introduced into the rule-based system, which can cause problems for subsequent maintenance. Subjects perceived the structured rule-base system as more complex than the object-oriented system, and perceived the object structure as more useful than the rule modularization and documentation     

网络告警知识发现研究与实现

文章研究企业网络告警数据中的知识发现问题,设计并实现了以Apriori算法为核心的网络告警关联规则发现系统。系统试运行结果表明,该系统能够有效发掘隐藏在海量告警数据背后、不易为网络管理人员所知的告警及故障模式知识。将发现的新知识应用到告警关联/故障诊断专家系统,有效突破了专家系统“知识获取”瓶颈,显著增强了专家系统推理和诊断网络故障的能力。     

Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base

Condition-based maintenance has attracted an increasing attention both academically and practically. If the required physical models to describe the dynamic systems are unknown and the monitored information only reflects part of the state of the dynamic systems, expert knowledge is a source of valuable information to be used. However, expert knowledge is usually in a qualitative form, and therefore, needs to be transformed and combined with the measured characteristic information to provide effective prognosis. As such, this paper focuses on developing a novel approach to deal with the problem. In the proposed approach, a belief rule base (BRB) for the failure prognostic model is constructed using the expert knowledge and the analysis of the failure mechanism. An online failure prognostic algorithm is then proposed on the basis of the currently available characteristic variable information. The failure prognostic model is finally used in a condition based decision model to support the replacement decision of the dynamic systems. A case example is examined to demonstrate the implementation and potential applications of the proposed failure prognostic algorithm and the condition-based replacement model.     

用于专家系统规则库的冗余校验方法研究

产生式规则是目前应用较多的一种知识表示方法。在用于确定发酵过程生物量软测量混合模型结构的专家系统中,当向产生式规则知识库添加新的规则时,冗余的存在会影响推理的效率以及推理的准确性。提出了一种用于该专家系统规则库的冗余校验方法,给出了冗余规则的判别、冗余规则的处理以及冗余校验的实现方法。实验结果表明,该冗余校验方法可以根据输入条件和已有规则,判断出新添加的规则是否冗余,并在消除冗余对推理效率影响的同时,降低模型复杂度,有利于优化混合模型的结构。     

利用专家系统建模——对人工智能的应用研究

专家系统是为了方便对各种问题制定决策,模拟专家的推理而设计的计算机程序。专家系统在表现那些需要制定决策的问题解决任务中尤其有效。建构专家系统规则库的学生对任一知识领域内概念之间动态的、临时的关系都会进行反思性思考。建构专家系统用到的思维方式可能是各种认知工具中最难的,因为它需要形式推理与逻辑推理,建构专家系统需要智力上的参与和挑战。     

Optimization Models for Training Belief-Rule-Based Systems

A belief rule-base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule representation scheme is proposed to extend traditional IF-THEN rules. The belief rule expression matrix in RIMER provides a compact framework for representing expert knowledge. However, it is difficult to accurately determine the parameters of a belief rule base (BRB) entirely subjectively, particularly, for a large-scale BRB with hundreds or even thousands of rules. In addition, a change in rule weight or attribute weight may lead to changes in the performance of a BRB. As such, there is a need to develop a supporting mechanism that can be used to train, in a locally optimal way, a BRB that is initially built using expert knowledge. In this paper, several new optimization models for locally training a BRB are developed. The new models are either single- or multiple-objective nonlinear optimization problems. The main feature of these new models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune a BRB whose internal structure is initially decided by experts' domain-specific knowledge or common sense judgments. As such, a wide range of knowledge representation schemes can be handled, thereby facilitating the construction of various types of BRB systems. Conclusions drawn from such a trained BRB with partially built-in expert knowledge can simulate real situations in a meaningful, consistent, and locally optimal way. A numerical study for a hierarchical rule base is examined to demonstrate how the new models can be implemented as well as their potential applications.     

An introduction to expert systems

The paper considers the features of expert systems which make them of educational interest, and discusses issues of knowledge representation through rule sets, and how such systems can be used to give explanation and advice. Working systems are outlined in Medicine, Geology and Computing. Some limitations of expert systems are noted and their future potential assessed.     

一种面向对象产生式系统的体系结构和规则模型

近年来，面向对象专家系统的研究引起了人们的广泛模型，本文提出了一种新的面向对象产生式系统体系的结构，给出了这种体系结构的知识表示模型和模糊区配原则，并且引入了规则对象的概念，我们进一步提出了规则对象的并发控制算法，最后给出了解释策略。     

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。     

基于Ontology的专家系统研究

针对当前专家系统领域知识语义表示不精确及推理效率低的问题,提出一种基于Ontology的专家系统框架。根据规则结构建立规则模式Ontology和动作Ontology,将用户推理条件作为模式子Ontology,设计出基于名称和实例的相似度计算及Clips推理机制的专家系统推理算法。结合高性能切削加工应用背景给出一个应用实例,应用结果表明,该系统能够明显提高推理效率和推理结果的语义表达。     

A structure for expert systems maintenance

An approach to the design of maintainable expert systems is presented. Central to this approach is a conceptual model in which the data and knowledge are both modelled as formal “items” in a uniform way. “Objects” are introduced as “item building” operators. The notion of the “decomposition” of items and objects provides the foundation for a single rule of normalization. This single rule applies to all items and objects, including knowledge items, and is a non-trivial generalization of the traditional normal forms for database. Coupling relationships represent the necessary maintenance paths in the conceptual model. A complete characterization of coupling relationships is given, and the value of normalization to the reduction of maintenance costs is discussed.