Fuzzy concepts in expert systems

The authors present a comprehensive expert-system building tool, called System Z-II, that can deal with exact, fuzzy (or inexact), and combined reasoning, allowing fuzzy and normal terms to be freely mixed in the rules and facts of an expert system. This fully implemented tool has been used to build several expert systems in the fields of student curriculum advisement, medical diagnosis, psychoanalysis, and risk analysis. System Z-II is a rule-based system that uses fuzzy logic and fuzzy numbers for its inexact reasoning. It uses two basic inexact concepts, fuzziness and uncertainty, which are distinct from each other in the system     

Applications of a novel fuzzy expert system shell

Abstract: Much of the information resident in the knowledge base of a typical expert system is imprecise, incomplete or not totally reliable. The special features of a novel expert system shell based on fuzzy logic and numbers are presented. This rule-based system can deal with exact, inexact (fuzzy) and combined reasoning as well as uncertainty represented by fuzzy numbers. Natural language interface is built in naturally using fuzzy logic representation. Several application areas, namely, classification, risk analysis and information retrieval, are discussed with four appropriate sample expert systems actually built using this shell. Through these expert systems, the additional power and advantages over traditional expert systems are illustrated. It has been demonstrated that the introduction of fuzzy concepts into expert systesms is not feasible but highly desirable.     

Fuzzy concepts in an object oriented expert system shell

Fuzzy logic is one of the methods to model the vagueness and imprecision of human knowledge. Some rule-based expert system shells have been successfully developed and have demonstrated the power of fuzzy logic in dealing with inexact reasoning and rule inferences. However, using rules for knowledge representation is not structured enough. In addition, knowledge cannot be easily represented in an abstracted (hierarchical) from. In this article the introduction of fuzzy concepts into object oriented knowledge representation (OOKR), which is a structured knowledge representation scheme, is presented. A framework for handling all the possible fuzzy concepts in OOKR at both the dynamic and static levels is proposed. In order to handle the inheritance mechanism and to model the relations among classes, instances, and attributes, some new fuzzy concepts and operations are introduced. These concepts and operations are developed from the semantic meaning rather than by an ad hoc approach. A prototype of the expert system shell. System FX-I, has been successfully developed based on the above framework, showing the feasibility of handling inexact knowledge in a structural way.     

Ecological applications using a novel expert system shell

Much of the information used by ecologists in modelling and decision making is imprecise. The imprecision arises both from data that are inexact or incomplete and from the use of ecological principles that are sometimes less than fully reliable and may be conflicting. Nevertheless, expert ecologists are able to construct usable models and make decisions that are used to manage and control ecological resources. This paper describes a unique expert system shell, developed in conjunction with user ecologists, which incorporates features enabling ecologists to represent knowledge and uncertainty in their expert systems in a way that is natural and appropriate. The reasoning mechanism was similarly developed in conjunction with user ecologists. It produces solutions to a class of expert level problems along with explanatory mechanisms and an appropriate analysis of the reasoning process. Three expert systems have been constructed by ecologists using this expert system shell. This enabled the shell designers to evaluate features for inclusion in the shell. The successful use of the shell by the ecologists has shown that significant economies arise when expert system shell design is tailored to use by a specific class of experts, in this case ecologists.     

Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.     

FuzzyShell: a large-scale expert system shell using fuzzy logic foruncertainty reasoning

There exist in the literature today many contributions dealing with the incorporation of fuzzy logic in expert systems. However, unfortunately, much of what has been proposed can only be applied to small-scale expert systems; that is, when the number of rules is in the dozens as opposed to in the hundreds. The more traditional (nonfuzzy) expert systems are able to cope with large numbers of rules by using Rete networks for maintaining matches of all the rules and all the facts. (A Rete network obviates the need to match the rules with the facts on every cycle of the inference engine.) In this paper, we present a more general Rete network that is particularly suitable for reasoning with fuzzy logic. The generalized Rete network consists of a cascade of three networks: the pattern network, the join network, and the evidence aggregation network. The first two layers are modified versions of similar layers for the traditional Rete networks and the last, the aggregation layer, is a new concept that allows fuzzy evidence to be aggregated when fuzzy inferences are made about the same fuzzy variable by different rules     

A self-evaluating expert system for X-ray rocking curve analysis

X-ray rocking curve analysis is widely used in research and industry to investigate the perfection of a variety of natural and synthetic crystals. In this article a method is demonstrated for the effective self-evaluation of an expert system for x-ray rocking curve analysis. the method uses a combination of fuzzy logic and machine learning, the latter defined as a self-adaptive system that improves system performance over time. the heuristics of several experts are combined using rules, frames, and connection matrices. Each expert is weighted on the basis of experience and these credibility weights are used to influence the decision processes of the expert system. All weights are evaluated over time and the basis for evaluation is successful or unsuccessful expert system decisions. Individual rules are also evaluated and whenever a rule is shown to be ineffective it is hidden from the reasoning processes of the expert system. When new situations occur that have not been allowed for in the rules of the expert system, then existing rules are fine-tuned and changed to deal with these new facts. New rules are inferred and evaluated in the same way as the heuristics of the human experts. © 1994 John Wiley & Sons, Inc.     

Systemic approach to fuzzy logic formalization for approximate reasoning

L.A. Zadeh, E.H. Mamdani, M. Mizumoto, et al., R.A. Aliev and A. Tserkovny have proposed methods for fuzzy reasoning in which antecedents and consequents involve fuzzy conditional propositions of the form “If x is A then y is B”, with A and B being fuzzy concepts (fuzzy sets). A formulation of fuzzy antecedent/consequent chains is one of the most important topics within a wide spectrum of problems in fuzzy sets in general and approximate reasoning, in particular. From the analysis of relevant research it becomes clear that for this purpose, a so-called fuzzy conditional inference rules comes as a viable alternative. In this study, we present a systemic approach toward fuzzy logic formalization for approximate reasoning. For this reason, we put together some comparative analysis of fuzzy reasoning methods in which antecedents contain a conditional proposition with fuzzy concepts and which are based on implication operators present in various types of fuzzy logic. We also show a process of a formation of the fuzzy logic regarded as an algebraic system closed under all its operations. We examine statistical characteristics of the proposed fuzzy logic. As the matter of practical interest, we construct a set of fuzzy conditional inference rules on the basis of the proposed fuzzy logic. Continuity and stability features of the formalized rules are investigated.     

A comparative study of uncertainty methods for legal reasoning

This paper is based on the premise that legal reasoning involves an evaluation of facts, principles, and legal precedent that are inexact, and uncertainty-based methods represent a useful approach for modeling this type of reasoning. By applying three different uncertainty-based methods to the same legal reasoning problem, a comparative study can be constructed. The application involves modeling legal reasoning for the assessment of potential liability due to defective product design. The three methods used for this study include: a Bayesian belief network, a fuzzy logic system, and an artificial neural network. A common knowledge base is used to implement the three solutions and provide an unbiased framework for evaluation. The problem framework and the construction of the common knowledgebase are described. The theoretical background for Bayesian belief networks, fuzzy logic inference, and multilayer perceptron with backpropagation are discussed. The design, implementation, and results with each of these systems are provided. The fuzzy logic system outperformed the other systems by reproducing the opinion of a skilled attorney in 99 of 100 cases, but the fuzzy logic system required more effort to construct the rulebase. The neural network method also reproduced the expert's opinions very well, but required less effort to develop. ©1999 John Wiley & Sons, Inc.     

DF控制推理模型的研究

针对控制系统中对象的模糊性和动态性,基于动态模糊集(Dynamic Fuzzy Sets)及动态模糊逻辑(Dynamic FuzzyLogic)系统理论,给出DF控制推理模型的相关概念,如DF向量、DF语言变量、DF语言规则和DF蕴涵关系等,并在此基础上探讨基于DF语言规则的DF推理方法,最后通过实例说明这些概念和方法的应用。     

一种基于PROLOG的ES建造工具库的研究与实现

本文描述了一种基于ＰＲＯＬＯＧ的专家系统建造工具库ＰＴＥＳ的实验系统。ＰＴＥＳ是用ＰＲＯＬＯＧ编写的，该系统根据支持基于规则的知识表示及近似推理对ＰＲＯＬＯＧ的知识处理能力进行了扩充。ＰＴＥＳ的推理机制使用了可能性能逻辑及模糊集合理论作为其逻辑基础，并以一种形式化的方法提供了处理非确定事实及非确定规则的能力。     

Statistical expert systems

Abstract: Two types of expert system which involve statistical expertise are statistical consulting programs and programs which find patterns in databases. Consulting programs can now be built quickly using programming tools. Most expert systems include mechanisms for reasoning under uncertainty. Methods under investigation include fuzzy logic, Dempster-Shafer theory, Bayesian analysis and various ad hoc methods. Learning systems use statistics to infer inductive rules, and statistical reasoning can also be used to evaluate the performance of expert systems. The use of a prototype statistical expert system, XSAMPLE, is demonstrated, as a system to handle a consulting session with a statistically moderately advanced user.     

一种关于Agent的模糊认知逻辑

由于经典逻辑不能表示模糊事实,而且Agent也不能利用经典逻辑的方法从模糊事实里获取知识,为了克服经典逻辑方面的不足,提出了一种关于Agent的模糊认知逻辑.首先,介绍了模糊认知逻辑和规则,该方法用数字来表示模糊事实,用模糊逻辑来获取知识.其次,引入了一种反向推理逻辑,利用该方法研究了条件和结论的相关性.最后,主要介绍Agent采用模糊认知逻辑获取知识的过程.     

基于模糊推理的物流车辆故障诊断专家系统

物流车辆故障诊断专家系统可以对物流车辆的故障进行诊断和排除. 为了提高该系统快速、准确诊断的能力, 在分析物流车辆的故障模式和故障机理的基础上, 建立故障树, 采用改进的CLIPS可以进行正向、反向两种模糊推理机制, 同时建立知识库管理系统对模糊规则和事实进行管理. 研究结果表明: 改进的CLIPS与VC++的结合, 使物流车辆故障诊断专家系统拥有模糊诊断故障的能力, 提高了物流车辆故障诊断的智能化水平.     

Reasoning in inconsistent knowledge bases

Databases and knowledge bases could be inconsistent in many ways. For example, during the construction of an expert system, we may consult many different experts. Each expert may provide us with a group of rules and facts which are self-consistent. However, when we coalesce the facts and rules provided by these different experts, inconsistency may arise. Alternatively, knowledge bases may be inconsistent due to the presence of some erroneous information. Thus, a framework for reasoning about knowledge bases that contain inconsistent information is necessary. However, existing frameworks for reasoning with inconsistency do not support reasoning by cases and reasoning with the law of excluded middle (“everything is either true or false”). In this paper, we show how reasoning with cases, and reasoning with the law of excluded middle may be captured. We develop a declarative and operational semantics for knowledge bases that are possibly inconsistent. We compare and contrast our work with work on explicit and non-monotonic modes of negation in logic programs and suggest under what circumstances one framework may be preferred over another     

An Inference Microprocessor Design

This paper is concerned with the design of an inference microprocessor for production rule systems.Its implementation is based on both exact and inexact (fuzzy logic) reasoning,so it can be used for building various production rule systems.The methods of translating linguistically expressed rules into numerical representations are described and the hardware implementations are discussed.Finally, a parallel architecture for the inference microprocessor is presented.     

Knowledge-Driven versus Data-Driven Logics

The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data-driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions by putting forward fuzzy rules as a Janus-faced tool that may represent knowledge, as well as approximate non-linear functions representing data. This paper lays bare logical foundations of data-driven reasoning whereby a set of formulas is understood as a set of observed facts rather than a set of beliefs. Several representation frameworks are considered from this point of view: classical logic, possibility theory, belief functions, epistemic logic, fuzzy rule-based systems. Mamdani's fuzzy rules are recovered as belonging to the data-driven view. In possibility theory a third set-function, different from possibility and necessity plays a key role in the data-driven view, and corresponds to a particular modality in epistemic logic. A bi-modal logic system is presented which handles both beliefs and observations, and for which a completeness theorem is given. Lastly, our results may shed new light in deontic logic and allow for a distinction between explicit and implicit permission that standard deontic modal logics do not often emphasize.     

一种基于PROLOG的ES建造工具库的研究与实现

本文描述了一基于ＰＲＯＬＯＧ的专家系统建造工具库ＰＴＥＳ的实验系统。ＰＴＥＳ是用ＰＲＯＬＯＧ编写的，该系统根据支持基于规则的知识表示及近似推理对ＰＲＯＬＯＧ的知识处理能力进行了扩充。ＰＴＥＳ的推理机制使用了可能性逻辑及模糊集合理论作为其逻辑基础并以一种形式化的方法提供了处理非确定事实及非确定规则的能力。     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

Fuzzy set-based methods in instance-based reasoning

A formal framework of instance-based prediction is presented in which the generalization beyond experience is founded on the concepts of similarity and possibility. The underlying extrapolation principle is formalized within the framework of fuzzy rules. Thus, instance-based reasoning can be realized as fuzzy set-based approximate reasoning. More precisely, our model makes use of so-called possibility rules. These rules establish a relation between the concepts of similarity and possibility, which takes the uncertain character of similarity-based inference into account: inductive inference is possibilistic in the sense that predictions take the form of possibility distributions on the set of outcomes, rather than precise (deterministic) estimations. The basic model is extended by means of fuzzy set-based modeling techniques. This extension provides the basis for incorporating domain-specific (expert) knowledge. Thus, our approach favors a view of instance-based reasoning according to which the user interacts closely with the system