Expert systems: Present and future

The purpose of this paper is to review key concepts in expert systems, across the life cycle of expert system development. As a result, we will analyze the choice of the application area for system development, gathering knowledge through so-called knowledge acquisition, choosing a knowledge representation, building in explanation and verifying and validating the system. In addition, we analyze a number of different applications of expert systems across a broad base of application areas, including medicine, geology and business. Further, we investigate some of the extensions to and emerging areas associated with expert systems.

Communications of the ACM, 1986

International Journal of Engineering Sciences & Research Technology, 2013

One of the largest area of applications of artificial intelligence is in expert systems, or knowledge based systems as they are often known. This type of system seeks to exploit the specialised skills or information held by group of people on specific areas. It can be thought of as a computerised consulting service. It can also be called an information guidance system. Such systems are used for prospecting medical diagnosis or as educational aids. They are also used in engineering and manufacture in the control of robots where they inter-relate with vision systems.

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)

Journal of Policy Analysis and Management, 2007

One manifestation of the "information society" is the increasing use of computers for management and decision making as well as for routine tasks. Expert systems, wich apply artificial intelligence, have been widely touted as aids for decision makers. These systems can give advice, trace patterns of logic employed in decision making, instruct, plan, and control; increasingly they can learn as well. I introduce here the concept and uses of expert systems; subsequent articles in this collection provide illustrations of their uses in the public sector.' Automating Expertise On the surface a t least, an expert system consists of computer software that emulates the problem-solving abilities of an expert in some well defined area of expertise (a domain). Expert systems evolved in the 1970s when some researchers in the field of artificial intelligence realized that they could not easily emulate human intelligence in a machine, in part because they could not even define the components of human intelligence. Human expertise, in contrast, is more easily identified, especially if confined to a well-defined domain. For example, the performance of a tax advisor can be measured in terms of right and wrong conclusions and defined in terms of heuristics, reasoning mechanisms, and the character of the knowledge. Expert systems were intended, therefore, to emulate human expertise, putatively as a first step towards mimicking a broader range of human intelligence. They have now taken on a life of their own, with considerable resources devoted to defining various kinds of expertise, identifying problems that lend themselves to embodiment in expert systems, and extending the range of expertise that can be computerized.

1989

: This appendix provides assistance in selecting the most appropriate tool for a particular expert system development project. A strong foundation in the usage of any specialized tool (e.g., chemical assay equipment or an expert system shell) is prerequisite to tool selection. Familiarity and reasonable facility with expert systems technology and knowledge engineering are necessary prior to applying the techniques presented in this appendix. This appendix outlines a selection method that was adapted from one developed at The RAND corporation for expert system tool evaluation. It is applicable after the decision has been made that such a tool is needed and the characteristics of that need are known. In particular, this involves (1) understanding the problem that shows potential for applied expert system technology and (2) choosing the best solution alternative. The term expert system means a system built using a knowledge-based approach to software development that applies expert kno...

in: Hans Jürgen Ohlbach (ed.), GWAI-92: Advances in Artificial Intelligence 16th German Conference on Artificial Intelligence Bonn, Germany, August 31 – September 3, 1992 Proceedings, 1993

The euphoric period in the history of expert systems has definitely come to an end. It is presently time to review the efforts which have been made in this field. Oriented to our conceptual framework of knowledge transformation, some trip wires which can seriously jeopardize the success of expert-system-developing projects are described in the following.

Expert Systems with Applications, 1995

R&D Management, 1985

Using expert system technology in industrial applications often involves the construction of programs of which the knowledge base is only a part and which have to be integrated into existing hardware and software environments. At Shell Research at Thornton it has been found that even within well explored areas such as fault diagnosis there is a need to incorporate non-expert tools within a knowledge system in order to perform tasks such as database management, screen handling and substantial numerical calculation. This is not always easy. A system to determine diesel engine condition from an analysis of the used oil has been developed using the SAGE package and subsequently redeveloped in Prolog to overcome such integration problems. An interesting side effect has been the discovery of the usefulness of Prolog in solving long standing problems concerning user access to complex relational databases.

European Journal of Operational Research, 1993

This paper provides a comprehensive bibliography of the application of expert systems (ES) in business areas from 1975 through 1989. Initial compilation of ES application was based on the references of major ES and other related textbooks. From 1984 to 1989 the most important source of information has been the Compact Disc-Read Only Memory (CD-ROM) version of ABI/INFORM database by University Microfilm International (UMI)/Data courier. One hundred and ninety-seven application articles are classified according to eight functional areas of business. This survey also identifies the top 21 journals that have published ES application articles as well as annual counts of those articles over the last 15 years.