Expert system-user integrated adaptive systems

Many expert systems operate in dynamic environments where various pertinent environmental variables and conditions vary with the passage of time. These environmental variables and conditions may affect both the set of conditions applied to input variables of expert systems and the set of recommendations provided by expert systems. For this reason, expert systems developed according to dynamic structure will generate timely recommendations. To incorporate dynamic characteristics into the structure of expert systems, it is necessary to develop expert systems as adaptive systems. This paper intends to integrate concepts of learning and adaptiveness into expert system technology.

Expert systems used to assist loan officers in improving the decision-making process of commercial loans are typical examples of expert systems that operate in dynamic environments. This paper illustrates that the quality of information provided to loan officers by expert systems may be improved when expert systems are designed as adaptive expert systems.     

EXPERT SYSTEMS FOR BUSINESS APPLICATIONS

Abstract

Expert systems are becoming more prevalent in financial and business applications. With venture capital pouring into the expert systems area, there is greater interest in expert systems by commercial firms. Companies are either building in-house expert systems capabilities or contracting out to expert system developers to construct expert systems for business applications. This paper presents much of the work being done in developing expert systems in business and discusses fertile areas for constructing more business expert systems.     

Expert systems—another frontier for industrial engineering

Expert systems are receiving increasing attention both in the academic and the practical words. This article examines the following area of potential interaction between industrial engineering and expert systems: except systems as a consultant, expert systems as a modeling tool and its integration with decision support systems, expert systems as a tutor, and the contribution of expert systems to the implementation of solutions based on quantitative analysis. Representatives examples of relevant systems are cited. Mutual benefits are expected as a result of the interaction between industrial engineering and expert systems.     

Expert systems and optimization

A problem-solving approach involving the integration of expert systems and optimization techniques is presented. A class of expert systems called tandem expert systems is introduced. Three variants of the tandem expert system, the data-reducing, model-based, and model-modifying expert systems, are explained with examples. It is emphasized that optimization techniques can be used more frequently in future expert systems     

Using human factor guidelines for developing expert systems

Abstract: Although MIS professionals had predicted that expert systems would improve productivity enormously, and a number of expert system application success stories have been reported, the expert system revolution has not yet happened. Moreover, there have been many cases, less well publicised, where expert systems have failed. Most problems concerning expert systems failure stem from non-technical issues such as cognitive and psychological problems, rather than such purely technical issues as an inference engine and an expert system shell. Here, the major reasons for expert systems failure and the need to consider human factors are discussed. We then propose human factor principles that can help designers handle most of these non-technical problems and many technical ones elegantly, improving the performance and acceptance of expert systems. Finally, some human factor guidelines for expert systems are presented so that these considerations may be incorporated into expert systems development in a clear and comprehensible manner.     

The client-centred approach: expert system maintenance

Abstract: Developing an expert system is a considerable investment in time and money and yet very little attention has focused on keeping expert systems in regular beneficial use. The problems of maintaining expert systems and the lack of research that this area has attracted are major contributing factors to the continued lack of acceptance of these systems. This paper discusses the maintenance of expert systems within the context of the Client Centred Approach. Although there are many similarities between maintaining expert systems and conventional systems maintenance there is one vital difference—knowledge changes. Consequently an expert system requires regular evolutionary maintenance if its performance is not to be impaired.
This paper is intended to promote discussion of maintenance issues within the community. We review some principles and techniques that can assist the maintenance of expert systems and describe some formative work towards a maintenance methodology derived from a case study of maintaining a commercially-available expert system.     

Expert systems on microcomputers*

Abstract: The increasing popularity of expert systems has led to a demand to apply expert systems technology in a wide variety of computing environments. As a result, various efforts have been made to implement expert systems on microcomputers. This article reviews some of the ongoing work on tools for the development of microcomputer-based expert systems. Some specific application areas are noted, and a brief discussion of the advantages and disadvantages of implementing expert systems on microcomputers is presented.     

The role of the industrial engineer in developing expert systems

The most popular area of Artificial Intelligence application today is in expert systems. This paper contains a discussion of expert systems, otherwise known as knowledge-based systems and knowledge systems. The principal components of an expert system, and the evolution of expert systems are presented. The suitability of a task to an expert system is proposed. When a task is suitable for an expert system application, the system must be developed by a knowledge engineer. The methodology that the knowledge engineer must go through to develop an expert system is demostrated. Industrial engineers have formal training in many areas which can be useful when assumming the role of knowledge engineer. These areas of industrial engineering and how they are beneficial is discussed. What the future may hold in store is also pondered.     

Parallel implementation of rule-based expert systems for interactive applications

A novel approach to parallel implementation of rule-based expert systems is presented in this paper. This approach is tailored for expert systems in interactive applications. Rule-based expert systems are modeled by state space and AND/OR graphs. The interdependences among rules are analyzed to guide rule-base partitioning and memory bank assignment. Parallel execution of rule-based expert systems is investigated in the environment of closely coupled multiprocessors. Algorithms are developed for the parallel execution of rules and for the allocation of data to memory banks in interactive applications.     

基于模糊神经网络的专家系统及应用

专家系统前景广阔，但其进一步发展必须引入其他先进技术，神经网络和模糊系统是解决知识获和经验性知识的不确定性等传统专家系统的“瓶颈”问题的全新途径。     

Military applications of expert systems

The advent of expert systems has led to the development of advanced computer systems in areas of medicine, geology, mathematics, chemistry, vision, speech and electronics. The recent acceptance of artificial intelligence as an appropriate technology for military applications has evolved into the development of a number of defense related expert systems. This paper reviews several military applications of expert systems in the areas of advanced visual target recognition, autonomous tactical vehicles, and combat pilot aid systems. System concepts for these areas are described. Several references are provided for each. A number of other military applications of expert systems are discussed through the paper.     

Expert system problem selection: A domain characteristics approach

The first generation of commercial expert systems based on AI technology are now available in the market place. But in the available literature, one can find hardly any material on expert system problem selection. In this paper a number of popular and successful expert systems are analyzed. Domain-dependent and domain-independent problem characteristics have been identified, based on the analysis. To test our contention that these characteristics significantly contribute to the success of expert systems, a questionnaire survey involving a number of expert system developers was conducted. Based on this, a domain characteristic approach for expert system problem selection is presented.     

专家系统研究现状及展望*

总结了自2000年以来专家系统在国内外的发展及应用情况,并根据知识表示方法的不同,对专家系统在各个领域的应用进行分类。结果显示：近五年来,伴随网络时代的到来及蓬勃发展,国内外专家系统发展迅速,一些新技术已经应用于专家系统,尤其是XML和W eb技术的应用,为专家系统的研究注入新的活力。同时对目前专家系统存在的一些问题以及今后的研究和发展进行了探讨。     

Expert systems in manufacturing: Promises and perils

Expert systems play a key role as a tool to enhance productivity, improve quality, increase profits and capture expertise in many business and industrial environments. Although the potential of expert systems is well-understood, it has been a challenging, and often elusive, task to make an expert system operational in the “real-world” environment. One of the major reasons why many expert systems fail to be integrated into the operational environment is a lack of knowledge and techniques on how to test an expert system. The implications of an inadequately-tested expert system in a manufacturing environment are serious. In this paper, we take a look at the many applications of expert systems in manufacturing and utilize a three-pronged approach to develop robust and reliable expert systems.     

Commercial expert systems: How to avoid the pitfalls

A rapidly growing number of commercial expert systems are becoming available. In the UK these are mostly PC-based, bringing expert systems within the reach of most businesses. It can be difficult to sort out the useful from the inadequate. It is also important only to use expert systems where they are really needed.     

Standards for evaluating expert system tools

A brief survey of the literature and proposal for a standard methodology for evaluating expert system building tools are described. Criteria for expert systems environmental factors and expert systems tool features are also discussed.     

Expert systems for small businesses

Small business contribute significantly to the economy. New information system technologies, like expert systems have the promise of improving the efficiency and competitiveness of small businesses. This paper reviews the issues related to application of expert systems in a small business and discusses their capabilities and limitations. The role of decision support systems and integration of DSS and expert systems as a useful tool for small businesses is also examined. Further, the issues concerning the selection of expert systems for a small business are described.     

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.     

Expert system technology transfer strategies: Selected cases from the United States and Mexico

Expert systems are being developed in a multitude of domains worldwide. The usage of expert systems within organizations is growing; however, many expert systems projects still fail due to poor ‘institutionalization’ practices. This paper addresses various strategies for providing the transfer of expert systems technology within organizations. Specifically, this paper will address expert system technology transfer strategies using examples from United States and Mexican organizations.