规则库冗余性控制策略的研究

冗余性控制是研究知识库组织、管理和维护中的一个问题．本文通过对智能型机译系统中规则知识表示方法的分析，提出了将冗余规则划分为显式冗余规则和隐式冗余规则分别予以处理的思想，给出了显式冗余规则的判别算法和部分隐式冗余规则的检测标准，并提出了控制机译系统规则库冗余性的基本原则.     

初中几何专家系统中的知识获取及实现

描述了初中几何专家系统中知识获取及实现的一般过程,指出了知识获取及实现中的难点和重点.由于研究问题的复杂性,专家系统规则库中规则量往往十分庞大,这给规则库的管理和维护带来很大不便.专家系统知识库的冗余性是影响系统运行效率和知识库维护的一个重要方面,针对一个具体的专家系统--平面几何智能解题系统,分析了关于知识库规则生成时效率低的问题,然后利用基于粗糙集的约简理论来消除和减少规则库的冗余,使得系统规则库中的规则精炼、简洁,易于维护,同时大大提高了系统的效率.     

图论在产生式系统知识库维护中的应用

当前的专家系统多用产生式规则来表示知识,但是,知识的不一致性和冗余性往往使得专家系统的维护较为困难。本文针对产生式知识库中知识的不一致性和冗余性,提出了一种利用图论来消除这种不一致性和冗余性的方法,并给出了相应的检查算法。该方法思路直观、易于实现,对于产生式系统知识库的维护具有一定的实际意义。     

一种基于Rough集的知识库冗余性化简研究

专家系统知识库的冗余性是影响系统运行效率和知识库维护的一个重要方面。针对一个具体的专家系统——平面几何智能解题系统，分析了关于知识库规则生成时效率低的问题，采用基于粗糙集的化简方法，简化了系统的知识库，减少了知识库的冗余性，提高了系统的效率。     

基于专家系统的减速离合器故障诊断研究

介绍专家系统的原理.并把专家系统方法应用在减速离合器故障信号诊断中,实现了基于专家系统的产品故障诊断功能.应用故障树分析法建立专家系统的知识库部分,通过故障树定性分析,再将简化的故障树用于基于规则的专家系统的知识库,既能解决知识获取的困难,又能简化知识库,降低知识的冗余,有利于系统的快速诊断.     

专家系统中知识库的维护

从添加规则、修改规则、删除规则等方面提出了知识库的维护方法,给出了知识库中冗余规则、矛盾规则、循环规则和孤立规则的检测方法。     

初中几何专家系统中的知识获取及实现

描述了初中几何专家系统中知识获取及实现的一般过程,指出了知识获取及实现中的难点和重点。由于研究问题的复杂性,专家系统规则库中规则量往往十分庞大,这给规则库的管理和维护带来很大不便。专家系统知识库的冗余性是影响系统运行效率和知识库维护的一个重要方面,针对一个具体的专家系统——平面几何智能解题系统,分析了关于知识库规则生成时效率低的问题,然后利用基于粗糙集的约简理论来消除和减少规则库的冗余,使得系统规则库中的规则精炼、简洁,易于维护,同时大大提高了系统的效率。     

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。     

一阶逻辑知识库的检测

本文提出的方法是以Loveland的MESON一阶逻辑定理证明过程为基础,用于一阶逻 辑规则知识库的冗余性和不一致性的检测.知识库的规则可包含非真、或及if-and-only-if 规则.系统以交互形式从正、反向推理研究知识库规则增加时的变化.     

规则库的合并以及冗余、环路和冲突的检测

针对知识库的建立需要耗费大量的时间和人力,同时相同或相似领域的知识库数量越来越多,提出利用现有规则知识库进行合并生成一个新的规则知识库,并对生成的新规则知识库进行知识冗余、环路和冲突的检测算法。首先,规则库利用有向超图来表示;其次,将有向超图利用其邻接矩阵来表示,那么规则库的合并可以转换成有向超图所对应的邻接矩阵的合并,并依据邻接矩阵求可达矩阵以及利用总可达矩阵来检测规则库中规则的冗余、环路和冲突。最后,算法的有效性通过实例加以验证。     

Reusing JessTab rules in Protégé

Protégé provides a complete ontology and knowledge base management tool. Along with JESS, JessTab provides one method of rule-based reasoning over a Protégé ontology and knowledge base. However, once JessTab rules have been created for a knowledge base, they are explicitly tied to it as they name particular classes and slots, which greatly hinders their reuse with further knowledge bases. We have developed a two-phase process and a supporting tool to support the reuse of JessTab rule sets. The first phase involves changing the class and slot references in the rule set into an abstract reference; the second phase involves automatically mapping between the abstract rules and further knowledge bases. Once mappings have been defined and applied for all the classes and slots in the abstract rules, the new rule set can then be run against the new knowledge base. We have satisfactorily tested our tool with several ontologies and associated rule sets; moreover, some of these tests have identified possible future improvements to the tool.     

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.     

Patterns of interaction in rule-based expert system programming

We study the effect of adding a rule to a rule-based heuristic classification expert system, in particular, a rule that causes an unforeseen interaction with rules already in the rule set. We show that it is possible for such an interaction to occur between sets of rules, even when no interaction is present between any pair of rules contained in these sets. A method is presented that identifies interactions between sets of rules, and an analysis is given which relates these interactions to rule-based programming practices which help to maintain die integrity of the knowledge base. We argue mat the method is practical, given some reasonable assumptions on the knowledge base.     

Designing fuzzy inference systems from data: Aninterpretability-oriented review

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined     

基于GA和机器学习的启发式规则调度方法

采用基于遗传算法的启发式规则的新型调度方法来处理可变工艺路径的调度问题,同时建立起启发式调度规则库和用于选择规则的知识库,并利用机器学习和模糊推理机制进行样本与知识库的匹配,实现高效实用的调度。计算实例表明了该算法的优越性能     

一种快速模糊推理系统

提出一种新的模糊推理系统，其模糊知识库具有紧致模糊规则库，即规则集为仅存储规则后的完全规则集，推理过程中可以根据当前输入信号值直接寻址被激励的模糊规则，从而只是有选择地执行被激励的规则，其优点是可以提高模糊推理速度，减少规则库存储容量，针对模糊芯片的VLSI实现，提出了可以根据输入信号值直接寻址被激励规则的电路。     

Extraction of rules from natural objects for automated mechanicalprocessing

In process applications, fast and accurate extraction of complex information from an object for the purpose of mechanical processing of that object, is often required. In this paper, a general rule-based approach is developed using a database of measurable geometric “features” and associated complex information. The rules relate the features to the complex processing information. During the on-line processing, the object features are measured and passed into the rule base. The output from the rule base is the complex information that is needed to process the object. A methodology is developed to generate probabilistic rules for the rule base using multivariate probability densities. A knowledge integration scheme is also developed which combines statistical knowledge with expert knowledge in order to improve the reliability and efficiency of information extraction. The rule generation methodology is implemented in a knowledge-based vision system for process information recognition. As an illustrative example, the problem of efficient head removal in an automated salmon processing plant is considered     

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.     

Checking the coherence and redundancy of fuzzy knowledge bases

Checking the coherence of a set of rules is an important step in knowledge base validation. Coherence is also needed in the field of fuzzy systems. Indeed, rules are often used regardless of their semantics, and it sometimes leads to sets of rules that make no sense. Avoiding redundancy is also of interest in real-time systems for which the inference engine is time consuming. A knowledge base is potentially inconsistent or incoherent if there exists a piece of input data that respects integrity constraints and that leads to logical inconsistency when added to the knowledge base. We more particularly consider knowledge bases composed of parallel fuzzy rules. Then, coherence means that the projection on the input variables of the conjunctive combination of the possibility distributions representing the fuzzy rules leaves these variables completely unrestricted (i.e., any value for these variables is possible) or, at least, not more restrictive than integrity constraints. Fuzzy rule representations can be implication-based or conjunction-based; we show that only implication-based models may lead to coherence problems. However, unlike conjunction-based models, they allow to design coherence checking processes. Some conditions that a set of parallel rules has to satisfy in order to avoid inconsistency problems are given for certainty or gradual rules. The problem of redundancy, which is also of interest for fuzzy knowledge bases validation, is addressed for these two kinds of rules     

A Strategy of Dynamic Reasoning in Knowledge-Based System with Fuzzy Production Rules

Fuzzy production rules have been successfully applied to represent uncertainty in a knowledge-based system. The knowledge organized as a knowledge base is static. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a strategy to reflect the dynamic nature of a system when we make reasoning with a knowledge-based system.This paper proposes a strategy of dynamic reasoning that can be used to takes account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy rules. A degree of match (DM) between actual input information and antecedent of a rule is represented by a value in interval [0, 1]. Weights of relative importance of attributes in a rule are obtained by the AHP (Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected to reasoning in knowledge-based system with fuzzy rules.With the proposed reasoning procedure, a decision maker can take his judgment on the given decision environment into a static knowledge base with fuzzy rules when he makes decision with the knowledge base. This procedure can be automated as a pre-processing system for fuzzy expert systems. Thereby the quality of decisions could be enhanced.