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.     

Fuzzy decision tables for expert systems

The use of fuzzy decision tables as a programming language for representing both the knowledge and the procedures in expert systems is discussed. Examples of their use for the generation of procedural code and for the generation of if-then rules are given.     

Decision support system concepts in expert systems: An empirical study

This paper reports on a survey carried out on 67 ES to investigate whether they employ DSS concepts. The author examines several DSS frameworks and proposes a questionnaire model to conduct the survey. The survey data are analysed and the paper concludes that the three fundamental DSS issues: semi-structured task, support, and effectiveness are explicitly applied in ES. It shows also that although both DSS and ES have similar aims, they accomplish them in completely different ways. The differences are in the boundary of problem space and the way to tackle problems.     

Fuzzy expert systems and challenge of new product pricing

This paper is focused on the representation and treatment of knowledge and data uncertainty within the context of an important industrial challenge, i.e., new product pricing. The most well known participating factor in pricing process is cost meanwhile the other factors like customer value and firm’s strategy should be considered in the pricing process, as well. Besides, there are other important factors like the risks that consumer bear in purchasing new product which must be carefully analyzed and considered. Nonetheless, many of these factors are blended with uncertainty. In recent decades, fuzzy logic was well developed and implemented in many applications to treat vagueness in complicated systems. Finding the pricing process a critical and complicated process which includes many vague parameters, we tried to design a fuzzy expert system to cope with this challenge. In this paper, after a brief introduction of fuzzy logic which has revealed a methodology to work with uncertainty and imitate humans reasoning, the pricing factors are introduced. Then a fuzzy expert system is designed to find the appropriate price of the new product considering the related parameters.     

Fuzzy expert system design for operating room air-condition control systems

In this study, a controlled fuzzy expert system (FES) was designed to provide the conditions necessary for operating rooms. For this purpose, existing operating rooms have been studied to see if there are more useful, reliable and comfortable ones. How an operating room can be controlled with FES and its advantages and disadvantages have also been researched. For a theoretically visible FES to show system’s advantage a prototype operating room was built and a suitable configuration was designed. In this system, heat, humidity, oxygen and particles were used as input parameters, and a fresh air entrance and fan circulation were chosen as output parameters. With the help of an expert, appropriate linguistic expressions and the membership function of these expressions were defined. The sensors were classified and sensor information was transferred to computer by means of an interface designed. In order to transfer the data to the system simultaneously, an interface was written in C#. Whether it provides the most suitable control for the system prototype was determined by simulating the operation with varying numbers of patients and operation personnel. In these trials, input, output and other necessary parameters were collected in the computer.In the study, we obtained excellent results in prototype operating room control with FES. The analyses of the results carried out indicated that the controls performed with FES provide more economical, comfortable, reliable and consistent controls and that they are feasible in a real operating room.     

Fuzzy expert systems and GIS for cholera health risk prediction in southern Africa

Cholera (Vibrio cholerae) is endemic in southern Africa and frequently breaks out in epidemics along the eastern seaboard. Extensive resources are directed at combating cholera yet it remains a significant problem. Limited resources could better be directed to prevent outbreaks if it were possible to assess the risk of an outbreak in space and time. The CSIR in South Africa is investigating technologies to predict health risk in line with national priorities. This paper describes an early warning GIS prototype tool aimed at identifying favourable preconditions for cholera outbreaks. These preconditions were defined using an expert system approach. The variables thus identified were input into a spatial fuzzy logic model that outputs risks. The model is based on the assumption that endemic reservoirs of cholera occur and that environmental conditions, especially algal blooms, trigger Vibrio growth in the natural environment. If the preconditions are met, the subsequent spread of cholera depends mainly on socio-economic factors such as human behaviour and access to safe water supply and sanitation. This paper focuses on the environmental preconditions. The methodology described relies on capturing expert knowledge and historic data that integrate climatic and biophysical parameters with epidemiological data to produce a fuzzy surface of cholera outbreak risk potential.     

Multi-domain expert systems

Abstract: The current trend in expert system building is domain-specific, i.e. there is one expert system for each problem domain. The increased involvement of computers in the decision-making process will inevitably lead to increased demand for expert systems. Based on the current approach to expert systems building, there will be a further proliferation of domain-specific expert systems. This is because each application area produces an expert system tailored to its requirements. This manner of producing expert systems is inadequate and an increased expectation in the performance of expert systems will eventually call for a new approach to constructing them. This paper examines the growth in the use of expert systems, looks into the limitations and problems associated with present-day domain-specific expert systems and suggests a multi-domain expert system architecture as a solution to the problem of increasingly disjointed domain-specific expert systems resulting from uncontrolled proliferation.     

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.     

Internet-based expert systems

The Internet offers a large potential for delivery of various information-based services, including the services of intelligent applications. As the availability of the Internet has grown, its value as a medium for the delivery of expert systems in particular has increased. There are now a large number of expert systems available on the Internet, including applications in industry, medicine, science and government. Though the Internet provides several advantages for expert system development, it also presents some special problems. These advantages and disadvantages are explored in more detail in this paper. The paper also presents a review of several Internet-based expert systems with a representative sample of publicly available applications, and a discussion of typical tools for developing Internet-based expert systems. A case study of an Internet-based expert system is presented as well.     

Evaluating expert systems

Abstract: Evaluation is crucial for improving expert system design and performance. This paper stresses the need for considering system evaluation throughout the development process. It highlights the importance of evaluating system usability and discusses key usability issues. A number of basic evaluation methods are described, including interviews, questionnaires, observation, system logging, user diaries, laboratory experiments and field trials. Finally, the paper looks at evaluating systems within organisations, and assessing other long term effects of expert systems.     

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.     

Japanese work in expert systems

Abstract: Activities concerning expert systems in Japan are outlined. Expert systems are receiving increasing attention in Japan, together with fifth-generation computers. Rather than describing the details of individual systems, a variety of expert systems are briefly introduced with their aims and features.     

Experts' systems instead of expert systems

By studying several cases of expert systems' use, a variety of difficulties were identified as directly depending on specific characteristics of experts and their tasks. This concerns more than the questions: “May experts be replaced by machines?” or “Is experts' knowledge explicable?”. The organisational structure of their work as well as the cyclic, non-plannable way of their task performing have further relevance. The paper introduces the concept of experts' systems to deal with diversities of their expertise and complexities of their work. It draws a distinction between non-monotonic problem solving, exploration, medium and modification, and argues that these modes are not reducible to yet another improved input/output strategy or dialogue style but introduce additional functions supporting the human-computer interaction according to experts' needs. In the first few sections, the paper covers the theoretical and empirical results of our research, whereas Section 4 introduces our design suggestions for experts' systems.     

Persuasiveness of expert systems

Expert system advice is not always evaluated by examining its contents. Users can be persuaded by expert system advice because they have certain beliefs about advice given by a computer. The experiment in this paper shows that subjects (n = 84) thought that, given the same argumentation, expert systems are more objective and rational than human advisers. Furthermore, subjects thought a problem was easier when advice on it was said to be given by an expert system while the advice was shown in production rule style. Such beliefs can influence expert system use.     

Second generation expert systems

Second generation expert systems are able to combine heuristic reasoning based on rules, with deep reasoning based on a model of the problem domain. This solves a number of major problems of current expert systems, most importantly the problem of knowledge acquisition: second generation expert systems can learn new rules by examining the results of deep reasoning. The paper outlines the components of second generation expert systems and gives an example.     

Rule chaining in fuzzy expert systems

A fuzzy expert system must do rule chaining differently than a nonfuzzy expert system. In particular, any rule that can fire with a particular linguistic variable in its consequent must fire before any rule whose antecedent conditions depend upon the resultant fuzzy set value of the consequent linguistic variable is allowed to fire. The dependent rules would be considered in a chain with the fuzzy rules which generate or assert the needed fuzzy linguistic variable. A recent paper by J. Pan et al. (1998) points out that a version of the FuzzyCLIPS expert system shell does not operate with chained fuzzy rules as one would expect. They introduce FuzzyShell which is described as the only known shell to have the expected fuzzy rule chaining performance. We show several approaches to obtaining the desired behavior in FuzzyCLIPS. Further, a potential pitfall with the FuzzyShell approach to dealing with chaining is pointed out     

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.     

Testing expert systems in process control

Special features of process control expert systems (PCX) make it both necessary and feasible to test them in a comprehensive manner. FAITH, an automated regression testing environment for PCX, integrates five specially adapted testing techniques, exploiting external specifications gained from the process control environment. FAITH also offers a set of metrics which can be used to predict testability during the early phases of PCX development. FAITH has been developed for a major German chemicals company where it is routinely used for certifying PCX, and for guiding design-for-testability