Survey on Expert System

Artificial Intelligence in Engineering, 1989

What are expert systems? What are their purposes? What are the impacts resulting from their implementations? This book aims to answer these questions and more. Written by experts in the field, chapters cover various concepts related to expert systems, including computational intelligence, signal processing, real time systems, systems optimization, electric power systems, support vector machines, fault diagnosis, asset management, and smart cities. Chapters systematically discuss the thematic concepts of expert systems and present a broad range of technical and theoretical information. As such, in addition to being of interest to a professional audience, the book is useful as a text for undergraduate and graduate courses. The prerequisites for understanding this book's content are basic, requiring only elementary knowledge of computation. The first part of this book (Chaps. 1 -5) examines the various uses and applications of expert systems. These include support decision systems, alert diagnosis systems, rule-based expert systems applied on heterogeneous data sources, detection of insects in coffee agriculture, influence of linear/cyclic polyethylenes on the electric percolation of microemulsions, and proposition of multi-agent for modeling smart parking. The second part of this book (Chaps. 5 -8) presents solutions that comprise technologies of expert systems in electric power systems. It describes topics related to efficient asset management practices for power systems using expert procedures, intelligent systems for estimation of gases dissolved in insulating mineral oil from physicochemical tests, and systems based on computational intelligence to estimate failure rates in power transformers.

Since the beginning of the development of artificial intelligence until today, have been written many papers and books in which has been presented progression of artificial intelligence. Artificial intelligence is a relatively young science. Emerged during sixties of the 20th century and from then until today, on its development and improvement are working teams of scientists from all parts of the world in specially equipped laboratories. Over time and developing, the computer data processing is increasingly turning to symbolic data, and less toward numerical. Category of programs that deals with assisting in decision-making in well-defined areas of knowledge are called expert system. These are systems that mimic the knowledge of an expert (human). They are also the most prevalent area of the artificial intelligence. In this article we will explain the necessity of using expert systems in the artificial intelligence.

1998

this paper, we present a .new approach to enhancing an expert system with an explanation facility. The approach comprises both software components and a methodology for assembling the components. The methodology is minimally intrusive into existing expert system development practice

Expert Systems With Applications, 2005

This paper surveys expert systems (ES) development using a literature review and classification of articles from 1995 to 2004 with a keyword index and article abstract in order to explore how ES methodologies and applications have developed during this period. Based on the scope of 166 articles from 78 academic journals (retrieved from five online database) of ES applications, this paper surveys and classifies ES methodologies using the following eleven categories: rule-based systems, knowledge-based systems, neural networks, fuzzy ESs, objectoriented methodology, case-based reasoning, system architecture, intelligent agent systems, database methodology, modeling, and ontology together with their applications for different research and problem domains. Discussion is presented, indicating the followings future development directions for ES methodologies and applications: (1) ES methodologies are tending to develop towards expertise orientation and ES applications development is a problem-oriented domain.

The research implements a Case-based Reasoning method into an expert system to help mechanical team in an automobile service station in relation with making a specific decision to address customer complaints. A number of mechanistic criteria and potential alternatives are designed, using knowledge-based as a system backbone which is elaborated into four main general phases known as retrieving similar problems, reusing knowledge, revising solution and retaining experiences to fortify a best solution. Technically, an on line application system are constructed by Object-oriented Software Engineering (OOSE) model to serve end-users interactively. The result of this study indicates an on line expert system where the application not only can be effectively utilized by specialized team but also be able to serve as a knowledge bridge for other.

The paper describes the different classifications of expert systems. There are mainly five types of expert systems. They are rule based expert system, frame based expert system, fuzzy expert system, neural expert system and neuro-fuzzy expert system. We discussed the expert systems based on their knowledge representation, inference engine, working of the system and user interface. At the end, we provided comparative study of above five types of expert systems based on different parameters. We can say that choice of an expert system depends on the domain requirements. Keywords: rule based expert system, fuzzy expert system, frame based expert system, neural expert system, neuro-fuzzy expert system ______________________________________________________________________________________________

ABSTRACT In this particular paper we survey development of Expert System by methodologies and applications from 2005 to 2015 via a literature review of a theoretical and classification papers as a basis. The survey has actually been Dependent on a search in the papers for ‘Expert System’ in the Elsevier and IEEE. In accordance with coverage of 58 articles on Expert System applications, this paper surveys and classifies Expert System methodologies using two categories: Rule-based systems (RBS), Knowledge-based systems (KBS) along with their applications for various research and problem domains. In addition to, discussion has actually been presented, and it also suggests that, the subsequent trends is required to be taken into note very soon regarding the development of Expert Systems in methodologies and applications: first, Expert Systems methodologies are destined to develop through the use of expertise of Expert System applications in the domain. Secondly, it is suggested that different social science methodologies has got to be include to provide more opportunity for explore the methods that used to development of systems. Thirdly, the ability to continually change and get new understanding is the driving power of Expert System methodologies, and will be the Expert Systems applications of future works.

1984

Reluctantly, I must admit that this is a good book. Weiss and Kulikowski have admirably delivered what they promise: a simple, proven-effective means for building prototype expert systems. The authors have considerable experience and speak with authority. Their points concerning diverse problems, such as selecting applications, knowledge acquisition, and strategic issues such as controlling questioning are clear and useful. What I most like about this book is that it is not pretentious. It deals only with what the authors understand best about expert systems, and all of that is presented simply, with good examples. The book steers clear of academic arguments about knowledge representation, and this simplification seems appropriate for a practical engineer's guide. As a basic guide for designing expert systems, the book offers the classification model as a common theme for describing how certain expert programs solve problems. A classification expert system is one that selects an output from a pre-enumerated list of possible solutions that is built into the program. Weiss and Kulikowski present this model in a simple way, describing CASNET, PROSPECTOR, DART/DASD, and similar systems as examples. Problem definition, elements of knowledge, and uncertain reasoning are treated concisely. The brief discussion of traditional problem solving methods, such as decision theory, is valuable. EXPERT, a production rule language, is illustrated by a hypothetical car diagnosis problem as well as a model for serum protein interpretation. Of particular interest is a description of the ELAS system for oil well log analysis, which integrates EX-PERT with traditional analysis programs. The book concludes with an interesting, down-to-earth essay on the state of the art and consideration of the future. But for all its good sense and clear exposition, the book has two important limitations. First, the classification model presented here is weakly developed; it applies only to the simplest problems. Much more is known about classification from studies of human problem solving. The authors ignore cognitive science studies altogether and so leave out basic ideas that are relevant to designing expert systems. Even more serious, the authors advocate a rule-based programming style that I am afraid may become the FORTRAN of knowledge engineering. So much knowledge is left implicit or is redundantly coded that modifications and extensions to the program will be expensive-just like maintaining FOR-TRAN programs. If we want to make knowledge engineering an efficient, well-structured enterprise, we can only hope that approaches like those used in EMYCIN, EXPERT, and OPS5 will soon die out. Examples from this book make my point. I will consider the classification model first. It is noteworthy that the two AI researchers who first described expert systems in terms of classification- and Chandrasekaran (Chandrasekaran, 1984)-both had experience with pattern recognition research in Electrical Engineering. Some of the most informative parts of Designing Expert Systems relate expert system research to pattern recognition and decision analysis. What is lacking in this analysis is similar attention to the other fork of the evolutionary tree, studies of human problem solving in cognitive science. After all, the patterns of an expert system are not linear discrimination functions, they are concepts. Research concerning the nature of memory and learning of categories is relevant for designing expert systems. In particular, the hierarchical structure of knowledge, the nature of schemas as stereotypes, and the hypothesis formation process all have a bearing in how we design an expert system. Certainly, in the language of EXPERT, Weiss and Kulikowski have taken a big step beyond EMYCIN by structuring knowledge in terms of findings, hypotheses, and different kinds of rules relating them. They list three kinds of rules: finding -finding, finding -hypothesis, and hypothesis hypothesis. Thus, the classification nature of the problem solving method is revealed as a mapping of findings onto hypotheses. Moreover, Weiss and Kulikowski describe search of this knowledge network independently, so inference knowledge is not mixed with process knowledge. But their analysis stops here. Weiss and Kulikowski are right to put forth the classification model as a scheme for structuring expert knowledge, but they have not made any attempt to relate it to what is known about experiential human knowledge. Further analysis shows that there are common relations that underlie the rules (Clancey, 1984). For example, findings are related to each other by definition, qualitative abstraction, and generalization. Knowing this provides a basis for acquiring, documenting, and explaining finding/finding rules. Besides asking the expert, "Do you have any way to conclude about F from other findings?" the knowledge engineer could also say, "Do you know subtypes of F?" or "Given this numeric finding, do you speak in terms of qualitative ranges?" Similarly, hypotheses are related by subtype or cause. Rather than considering car failure diagnoses (an example developed in the book) as a simple linear list, the knowledge engineer can start with the assumption that the expert organizes his knowledge as a hierarchy of diagnoses. The classification model can be further refined in several ways. First, a distinction can be made between heuristic classification and simple classification by direct matching of features (as in botany and zoology). The pre-specified solutions in expert systems are often stereotypic descriptions, not patterns of necessary and sufficient features. This has important implications for knowledge acquisition and ensuring robustness in dealing with noisy data. Second, emphasizing rule implication alone, Weiss and Kulikowski fail to mention 84 THE AI MAGAZINE Winter, 1985 AI Magazine Volume 5 Number 4 (1984) (© AAAI)

Proceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96), 1996

This paper deals with a new methodology for developing an Expert System (ES). It has the ability of learning to extract knowledge from a poor knowledge base using the learning by example paradigms. The choice of a poor knowledge base was motivated by the fact that in this case it is easier to have consistence in putting together the several pieces of knowledge. So, the problems attached to knowledge elicitation are simplified.