• The Journal of Information Systems
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• v.5
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• pp.411-427
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• 1996

As the application of real time expert systems has been expanded, the real time expert systems have become an integral part of the information society. Real time expert systems are viewed as a new paradigm to integrate real time problem solving and expert system technology. However, a substantial amount of work is still required to effectively handle a new and challenging opportunity for successful implementation of real time expert systems. In this article, the basic concepts and characteristics of real time expert systems are discussed. The article also presents, through the literature survey, the summary of real time expert systems applications and a class of tools, providing an enabling real time based expert system technology. Finally, the article discusses a set of requirements which real time expert systems must satisfy.

• Journal of the military operations research society of Korea
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• v.16 no.1
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• pp.56-66
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• 1990

Expert systems are in an early evolutionary phase, but it already has significant impacts on business operations. More and more organizations are becoming interested in applying expert systems as solutions to their business problems. This paper describes how systems started, what is their current situation, and what are their application areas. It outlines what is expert system and how to develop an expert system. And it analyzes expert system shells and shows some current available expert system shells. Lastly, it concludes and predicts the main attributes of the future expert systems.

• The Journal of Information Systems
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• v.1
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• pp.95-109
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• 1992

A number of success story about various application areas including manufacturing, accounting, finance, education, and engineering are reported. MIS professionals predicted that expert systems would improve the productivity enormously. However, the expert system revolution has not happened yet. Although not reported in the open society widely, there are failure stories of expert systems. Most of problems concerning expert system failure stem from the non-technical issues such as cognitive and psychological problems rather than the technical issues. We hypothesize that human factor principle enables designers to handle most of these non-technical problems elegantly and to improve the performance and acceptance of the expert systems. Major reasons for expert system failure and needs of human factors are discussed. Human factor guidelines to expert system make the prospects of the expert systems with human factors clear and understandable.

• Journal of Korean Society for Quality Management
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• v.17 no.2
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• pp.113-120
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• 1989

The purposes of this paper are to compare expert systems and decision support systems, and illustrate the possible benefits when expert systems are integrated into the model base of a decision support systems for supporting decision-makers. Integrating expert systems capability into decision support systems may enhance the quality and efficiency of both computerized systems. This integration can improve selection of model, analysis, model management, judgement, and modeling. Thus the results are much more powerful decision support systems than are presently available.

• Journal of the Korean Society for Library and Information Science
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• v.14
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• pp.67-92
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• 1987

In this paper, artificial intelligence applications in library and information science are reviewed. Especially, natural language processing and expert systems are represented as the two major application areas. In natural language processing, natural language interface systems and .question-answering systems are discussed in detail with some specific examples. In the second part of the paper, online search intermidiary systems, reference expert systems, classification and cataloging expert systems are described as possible expert systems to be developed in libraries and information systems. As a conclusion, implications of the artificial intelligence applications for librarians and information scientists are suggested.

• Asia pacific journal of information systems
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• v.2 no.1
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• pp.93-103
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• 1992

In spite of the importance that MIS researchers place in Expert Systems, there is no agreement as to whether Schools of Business should include this topic in their curriculum. To answer this question, this study presents a survey of the use and impacts of Expert Systems in Organizations. The study also assesses the impact of training on users ' perceptions and use of Expert Systems. The survey indicates that Expert Systems are being used in several areas of industry and that Business schools would serve their student body better by exposing thenm to these tools. The study also reviews the alternatizes used by IBM MoIS Grant Schools for incorporating Expert Systems in their curriculum. A discussion of the cases where each alternative would be more appropriate is included.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.115-130
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• 2004

This paper proposes how to build an expert profile database in KMS, which provides the information of expertise that each expert possesses in the organization. To manage tacit knowledge in a knowledge management system, recent researches in this field have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise so that users can contact them for help. In this paper, we develop a framework to automate expert classification using a text categorization technique called Vector Space Model, through which an expert database composed of all the compiled profile information is built. This approach minimizes the maintenance cost of manual expert profiling while eliminating the possibility of incorrectness and obsolescence resulted from subjective manual processing. Also, we define the structure of expertise so that we can implement the expert classification framework to build an expert database in KMS. The developed prototype system, "Knowledge Portal for Researchers in Science and Technology," is introduced to show the applicability of the proposed framework.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.9 no.13
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• pp.23-28
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• 1986

The qualitative aspects of decision making have been the toughest problems that could nor be easily manipulated and solved by the traditional management science techniques. The expert systems has been emerged as a powerful tool for handling such difficulties. In this article, the concept, the structure of the expert systems would be reviewed and the expert systems application to management would be discussed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.49-52
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• 2003

In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert systems. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently, and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.231-240
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• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.