基于加权产生式规则知识库的不一致性和冗余性研究

一、问题的提出规则的产生式表示法是目前专家系统中最常用的一种方法,它易于表达浅层知识,并且具有模块性、清晰性、自然性等优点。但是,由于产生式系统的知识库常常是不完备的,需要对规则进行增、删、改等维护操作,这往往会引起知识库中知识的不一致性和冗余     

产生式知识库一致性和冗余性检查

本文介绍了目前应用较为广泛的产生式专家系统知识库的不一致和冗余的几种可能的规则或规则链形式,并给出了一致性和冗余性检查的技术和实现方法.这些技术和方法可以帮助知识工程师有效地建立和维护知识库,并为系统的自动知识获取提供了基础.     

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。     

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。     

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。     

粗糙集理论在故障诊断专家系统中的应用

针对专家系统中知识获取的瓶颈问题，引入了一种基于粗糙集理论的专家系统模型。该模型在知识入库前对其进行过滤，并利用粗糙集理论的约简算法消除知识库的冗余，从而实现了对知识库结构和性能的有效维护及完善。     

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.     

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。     

规则库的冗余测试

规则冗余会引起专家系统效率低下、增加维护代价等不利后果,在专家系统中,保持规则库的精练和简洁,避免规则冗余是规则库组织和管理中必须解决的首要问题。概述了冗余规则的分类,并利用这些规则通过算法对某农业专家系统进行测试,发现此知识库存在一定程度的冗余性,但事实证明要完全控制规则的冗余性是不可判定的。     

Developing maintainable expert systems using case-based reasoning

Abstract: Maintainability problems associated with traditional software systems are exacerbated in rule-based systems. The very nature of that approach — separation of control knowledge and data-driven execution — hampers maintenance. While there are widely accepted techniques for maintaining conventional software, the same is not true for rule-based systems. In most situations, both a knowledge engineer and a domain expert are necessary to update the rules of a rule-based system. This paper presents, first, an overview of the software engineering techniques and object-oriented methods used in maintaining rule-based systems. It then discusses alternate paradigms for expert system development. The benefits of using case-based reasoning (from the maintenance point of view) are illustrated through the implementation of a case-based scheduler. The main value of the scheduler is that its knowledge base can be modified by the expert without the assistance of a knowledge engineer. Since changes in application requirements can be given directly to the system by the expert, the effort of maintaining the knowledge base is greatly reduced.     

A software engineering methodology for rule-based systems

Current expert systems are typically difficult to change once they are built. The authors introduce a method for developing more easily maintainable rule-based expert systems, which is based on dividing the rules into groups and focusing attention on those facts that carry information between rules in different groups. They describe a new algorithm for grouping the rules of a knowledge base automatically and a notation set of software tools for the proposed method. The approach is supported by a study of the connectivity of rules and facts in rule-based systems; it is found that they indeed have the latent structure necessary for the programming methodology. Recent experimental results also support the approach. In contrast to the homogeneous way in which the facts of a rule-based system are usually viewed, this approach shows that certain facts are more important than others with regard to future modifications of the rules     

Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU

This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method.     

Robustness of decentralized knowledge systems: a grammar-theoretic view

This paper introduces the formal framework of grammar systems to handle a practical and important property of decentralized rule-based knowledge systems. The property is called robustness. In our framework, a rule-based system is robust when some rules can be removed from it and yet its critical functionality remains unchanged. As a theoretical framework for study robustness of decentralized rule-based systems we use grammar systems. We prove within that framework that the question whether a knowledge system is robust or not is undecidable. In contrast, we prove with the same framework that whether or not a component is ever enabled, or whether or not a component working in a special—so called maximal—mode blocks the further functioningof a systems when enabled, are decidable. Some open problems are also formulated.     

CLASP: integrating term subsumption systems and production systems

The general architecture and an implementation of a classification-based production system (CLASP) are presented. The main objective is to extend the benefits of classification capabilities in frame systems to the developers of rule-based systems. Two major processes of CLASP, a semantic pattern matcher and a pattern classifier, are described. The semantic pattern matcher extends the pattern matching capabilities of rule-based systems through the use of terminological knowledge. The pattern classifier enables the system to compute a rule's specificity, which is useful for conflict resolution, based on the semantics of its left-hand side. The paradigm not only enhances the reasoning capabilities of rule-based systems, but also helps to reduce the cost of maintaining such systems because definitional knowledge is explicitly represented in a form that facilitates sharing and minimizes duplication of effort     

CASE-BASED REASONING

Case-based reasoning (CBR) is an alternative to traditional rule-based expert systems approaches. The fact that rules can be incomplete and must be assembled through a time-consuming knowledge acquisition process has been a major bottleneck in the usefulness of rule-based methods for configuring information systems. Case-based reasoning systems, by contrast, access problem-solving experiences or cases for the next problem-solving situation. The CBR process, some commercial shells, and CBR's use to augment expert systems applications are discussed.     

Categorizing approaches combining rule-based and case-based reasoning

Abstract: Rule-based and case-based reasoning are two popular approaches used in intelligent systems. Rules usually represent general knowledge, whereas cases encompass knowledge accumulated from specific (specialized) situations. Each approach has advantages and disadvantages, which are proved to be complementary to a large degree. So, it is well justified to combine rules and cases to produce effective hybrid approaches, surpassing the disadvantages of each component method. In this paper, we first present advantages and disadvantages of rule-based and case-based reasoning and show that they are complementary. We then discuss the deficiencies of existing categorization schemes for integrations of rule-based and case-based representations. To deal with these deficiencies, we introduce a new categorization scheme. Finally, we briefly present representative approaches for the final categories of our scheme.     

Enhanced high-level Petri nets with multiple colors for knowledgeverification/validation of rule-based expert systems

Exploring the properties of rule-based expert systems through Petri net models has received a lot of attention. Traditional Petri nets provide a straightforward but inadequate method for knowledge verification/validation of rule-based expert systems. We propose an enhanced high-level Petri net model in which variables and negative information can be represented and processed properly. Rule inference is modeled exactly and some important aspects in rule-based systems (RBSs), such as conservation of facts, refraction, and closed-world assumption, are considered in this model. With the coloring scheme proposed in this paper, the tasks involved in checking the logic structure and output correctness of an RES are formally investigated. We focus on the detection of redundancy, conflicts, cycles, unnecessary conditions, dead ends, and unreachable goals in an RES. These knowledge verification/validation (KVV) tasks are formulated as the reachability problem and improper knowledge can be detected by solving a set of equations with respect to multiple colors. The complexity of our method is discussed and a comparison of our model with other Petri net models is presented.     

Fuzzy Interpolative Reasoning for Sparse Fuzzy Rule-Based Systems Based on ${bm alpha}$-Cuts and Transformations Techniques

In sparse fuzzy rule-based systems, the fuzzy rule bases are usually incomplete. In this situation, the system may not properly perform fuzzy reasoning to get reasonable consequences. In order to overcome the drawback of sparse fuzzy rule-based systems, there is an increasing demand to develop fuzzy interpolative reasoning techniques in sparse fuzzy rule-based systems. In this paper, we present a new fuzzy interpolative reasoning method via cutting and transformation techniques for sparse fuzzy rule-based systems. It can produce more reasonable results than the existing methods. The proposed method provides a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.