A Relational Database Interface for Design Expert Systems

An expert database system is an expert system which is developed on databases using DBMS technology to manage facts and rules. Frame-based expert systems are widely used as the knowledge representation for such expert systems with large knowledge bases. Many systems have the ability to connect to external databases. Facts stored in databases can be loaded into expert system's knowledge base and inference is performed by the inference engine of the expert system. In many cases, such external facts are required several times for each inference. Thus, a lot of communication traffic takes place. This research work present the design and implementation of a frame-based object-relational database system which has a tight coupling between the expert system and the external knowledge base. The external knowledge base also use frame as its knowledge representation. More over, it has its own inference engine so that inference can be perform on the knowledge base side and the results, not only simple facts, are sent back to the expert system for further inference. A medical consultation system is used as an illustrated example an Oracle lOg object relational DBMS is used as the database platform.

Although the use of expert systems (ESs) as a problem-solving tool is relatively new in the transportation profession, it has been recognized that a well-designed ES can contribute in areas where knowledge domain requires an integration of expertise from various disciplines or is very complex and difficult to quantify. To minimize the effort involved in developing essential ES mechanisms, researchers often prefer to use ES shells which provide a standard inference procedure, knowledge representation, and a user-friendly design environment. However, commercial ES shells tend to be designed for only certain classes of problems, and are not as general and flexible as programming languages. More than 150 commercial ES shells are available in the microcomputer market. Most ES shells are ususally claimed by their vendors to be usable for almost any conceivable applications, but such claims have often proved to be extravagant. Hence, the selection of an efficient yet cost-effective commercial shell is one of the most critical tasks in ES development. In reviewing the the vital task of ES shell selection, this paper presents our experience in evaluating more than 30 ES shells for developing an ES for signal design. The proposed ES includes signal settings, phasing plans, controllers, and detector locations.

Expert systems have now proven their value in a variety of enterprises ranging from medicine to geology. Small expert systems are at work in numerous commercial and industrial sites, but large systems are still rare. Besides the difficulties inherent in the collection of useful and correct domain knowledge, large knowledge bases present special problems in data management. These problems often cause a good pilot system to founder when expanded to an operational size.This paper addresses the difficulties of efficiently handling large knowledge bases. The methods discussed result from viewing a knowledge base as a database and analyzing the knowledge as one would analyze a database application, using Chen's Entity-Relationship (E-R) Diagrams [CHEN76] to build a relational database representation. Besides providing a different perspective on the management of knowledge bases, such a method facilitates the use of a commercial DBMS for this task.Following these ideas the simple assertions of the knowledge base are viewed as one entity type and the rules themselves as another, with IF and THEN being separate relationships between these two types. This decomposition is reasonable for capturing the knowledge represented by the rules and leads to a relational description which is considerably different than usual rule representations. More precisely, the E-R diagram of Figure 1 is taken as the basis of the model, where the rectangles represent entities and the ellipses represent relationships between the entities connected to that ellipse. The unboxed words such as rule# are attributes of the associated entities and correspond to the information to be saved in the database.The E-R diagram in Figure 1 can be converted to the relational database tables shown in Figure 2 where rule# is a name or a number that identifies the rule, if_part is a simple assertion Ai (antecedent), then_part is a simple assertion Ci (conclusion), and conf is a number representing the confidence in that deduction.This results in a very different data organization from the usual collection of knowledge found in rule-based systems. Beyond that analysis, very specific implementation techniques are suggested for knowledge bases, using both standard relational data bases and simpler file-handling techniques such as those available in standard programming languages. Caution must be taken, however, before simply trying to turn knowledge base management over to a commercial DBMS, since the behavior of “queries” within a rule base is very different from that of ordinary database queries. Comparisons are made to such recent works as those of Deering, Debanne, and Bouzegoub which suggest a variety of implementations for knowledge bases.Often in the modeling of a domain, one discovers a hierarchical relation among the basic concepts, vocabulary, or objects of study. Such an IsA or AKindOf hierarchy can be used to create a segmentation of the knowledge base resulting in significant efficiency improvements within the system. This possibility is explored in some detail.Throughout this work, emphasis is given to dynamic rule bases which are subject to frequent changes and which, therefore, require a generalized inference engine and flexible data structures. Often due to execution speed requirements some domain knowledge is encoded as procedures within the inference engine. Although this may improve the efficiency of the system, it undermines the generality and flexibility that are also required. This paper investigates a promising method for incorporating such procedural inclusions within the general knowledge base structure. The approach developed allows the absorption of changes without restructuring of the knowledge base or modifications to the inference engine, and maintains system transparency, simplicity, and expandability.

This article is an introduction to artificial intelligence (AI), especially the place of expert systems (ES) in AI. Some applications of expert systems in other industries are discussed, as well as potential areas of application within the pharmaceutical industry. Advantages and disadvantages of developing ES using so-called AI languages v ES shells are covered. A product review is presented, including AI languages, induction ES shells, straightforward rule shells, and sophisticated ES development environments. Also included is a discussion of the problems and pitfalls that may await anyone attempting to develop ES in pharmaceutical R&D. Finally, some recommendations are offered that they may be useful to those just getting started in this area.

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

Edamame grade is determined based on edamame quality parameters with several parameters that are quite a lot. The process of deciding edamame grade is relatively complicated and challenging. To determine the edamame grade, an experienced expert in the field is needed. With limited grading experts, the design of expert systems for grading edamame can help users or companies to determine edamame grade. This study aims to design an expert system for determining edamame grade, which can determine the quality of edamame accurately. The Design of an expert system in this research uses the forward chaining method. The stages of designing an expert system include problem identification, knowledge acquisition, building a data flow diagram, compile the database, building a decision tree diagram, and developing a user interface. The results showed that the expert system has been verified and validated well. Verification test results show that all the menu buttons on the expert system application are functioning correctly. Then, the validation results also show that the edamame quality output produced by the expert system is compatible with the expert solution, meaning that the expert system's output data is by following with the results of the decision given by the expert. In testing the validation of determining the quality is appropriate. The results of the verification and validation of the edamame grading expert system show that the expert system has produced satisfactory outcomes. Expert system output is compatible with the solution provided by the expert.

In this study contains an expert system design for web-based lecturer scheduling that can facilitate the Chair of the Study Program in scheduling courses for lecturers. This system was built using Notepad ++ software, XAMPP with HTML, CSS, JavaScript, PHP and database programming languages using MYSQL. Subjects in this study are expert system applications as the media schedule lecturer subjects. Data collection in this study uses the interview method and questionnaire method. This system is built with the Waterfall model namely analysis, design, coding and testing. This application in the inference engine used is the forward chaining method. The results of this study are expert system applications to schedule lecturers’ subjects according to the questionnaire data. Based on the results of the trial it can be concluded that this expert system application can facilitate the Chair of the Study Program in accurately scheduling courses, classes, days, hours, space, and lecturers

At school, especially in Automotive Vocational High Schools, a learning process is carried out by helping students to recognize and overcome problems that occur in motorbike/car damage. One of the SMKs that has an Automotive expertise program is SMK Negeri 2 Bengkulu City. So far, the automotive learning system has used books and pictures to support the learning process at school. However, this sometimes has problems, where students do not fully understand the material given, and on the other hand students have difficulty learning independently either in the school environment or at home. Expert systems in helping students diagnose car engine damage using the Expert System Development Life Cycle (ESDLC) method can be accessed online with internet access via the web link http://riska.vad.my.id. This expert system application is made using the PHP programming language and MySQL database. This expert system application has been embedded with the ESDLC method with the Forward Chaining inference engine which is used to trace damage to car engines based on the symptoms experienced by the car engine. This expert system application can help facilitate the understanding of students at SMK Negeri 2 Bengkulu City in diagnosing car engine damage, by providing consultation directions starting from entering symptoms to getting consultation results. Based on the system testing that has been done, it can be concluded that the functionality of the application has been running well and this expert system can provide consultation results based on the symptoms selected by the user through the stages of the Expert System Development Life Cycle (ESDLC) method.

Abstract: The First Symposium on The Theory and Application of Expert Systems in Emergency Management, held at the Department of Commerce, Washington, D.C. (April 24 and 25, 1985) was funded by the Federal Emergency Management Agency and organized by the National Bureau of Standards' Operations Research Division. The purpose of the symposium was to bring together researchers in expert systems, artificial intelligence, and emergency operations in a forum to review the concepts of expert systems and the problems of emergency management, with the objective of determining how expert systems can be used to augment the experience of local, State and Federal emergency managers faced with the difficult tasks of determining the best response to an emergency situation. Speakers addressed the following areas: The theory and uncertainty aspects of expert systems, artificial intelligence's future role in emergency management, technology for building and using expert systems, emergency management decisions and information needs and uses, applications of expert systems in the management of chemical spills and shipboard and coal mine fires, and the role and use of simulation in emergency management expert systems.

Humans seem to have a natural instinct for wanting to understand and make sense of their environment and things in it. In expert systems (ES), explanation can be used to clarify the reasoning process to users such that they can gain a better understanding of how the system functions. With the help of good explanation facilities, a user can know why an ES is asking a particular question, how the expert system will act if given a certain input, and how the ES reaches a particular conclusion. This is especially important when an ES application is used as a high level advisor to professionals who must retain responsibility for the decisions which are made. However, most ES explanation components require acquiring additional knowledge for explanation, thus increasing the effort of implementing an ES with explanation capabilities. The primary goal of this work is to present a methodology for automatically generating explanations during and at the end of the reasoning process. The developed explanation components can deal with different knowledge representation schemes that are used by problem solving methods namely, the and confirm hypotheses that is based on the CommonKADS methodology[1], and the routine design generic task[2]. As a proof of concept the explanation components were developed and integrated into the agricultural expert system generic tool (AESGT)[3]. The developed explanation components can be easily reused with expert systems developed by the tool to automatically generate explanation for the reasoning process.

Computation scheme for the general purpose VLSI fuzzy inference engine as expert system

The authors review and categorize the research in applications of artificial intelligence (AI) and expert systems (ES) in new product development (NPD) activities. A brief overview of NPD process and AI is presented. This is followed by a literature survey in regard to AI and ES applications in NPD, which revealed twenty four articles (twenty two applications) in the 1990–1997 period. The applications are categorized into five areas: expert decision support systems for NPD project evaluation, knowledge-based systems (KBS) for product and process design, KBS for QFD, AI support for conceptual design and AI support for group decision making in concurrent engineering. Brief review of each application is provided. The articles are also grouped by NPD stages and seven NPD core elements (competencies and abilities). Further research areas are pointed out.

Expert system applications in production and operations management: A selected bibliography (1975–1989)

Artificial intelligence (AI) has made enormous progress in recent decades, significantly influencing various industries, including the medical field. An expert system is a type of artificial intelligence that deliver specialized services in addition to experts by gathering specialty knowledge and expert information in a specific field. In light of the swift rise of Internet technology, the way expert systems are created and supplied has changed. The study used Google Scholar as the online database that provides published content about expert systems that apply AI in the medical field and covers the period of 2019 to 2024 to collect the latest data. The influence and effectiveness of utilizing an expert system with AI across various specializations are emphasized in this study's analysis of their uses in medicine. By using expert systems, they can mimic the decision-making of a human expert, improving healthcare results. Several medical specialties, including gynecology, neurology, orthopedics, ophthalmology, and cardiology, use these solutions based on AI. However, every field has its own set of difficulties, and technological developments are impacting the accuracy and reliability of diagnosis, especially gynecological diseases, which are more complex and diverse. Despite the difficulties, expert systems with AI can provide innovative solutions that can improve patient care. Understanding how AI works can pave the way for future innovation and progress in medical technologies, creating better patient results and more efficient healthcare systems. Educating medical personnel about AI and its applications could help them use these tools more efficiently to provide better patient care.

The control and monitoring of power generation plants is being complicated day by day, with the increase size and capacity of equipments involved in power generation process. This calls for the presence of experienced and well trained operators for decision making and management of various plant related activities. Scarcity of well trained and experienced plant operators is one of the major problems faced by modern power industry. Application of artificial intelligence techniques, especially expert systems whose main characteristics is to simulate expert plant operator’s actions is one of the actively researched areas in the field of plant automation. This paper presents an overview of various expert system applications in power generation plants. It points out technological advancement of expert system technology and its integration with various types of modern techniques such as fuzzy, neural network, machine vision and data acquisition systems. Expert system can significantly reduce the work load on plant operators and experts, and act as an expert for plant fault diagnosis and maintenance. Various other applications include data processing, alarm reduction, schedule optimisation, operator training and evaluation. The review point out that integration of modern techniques such as neural network, fuzzy, machine vision, data base, simulators etc. with conventional rule based methodologies have added greater dimensions to problem solving capabilities of an expert system. DOI: http://dx.doi.org/10.11591/ijece.v4i1.5025