Expert systems: An overview (original) (raw)
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Expert System-A Review Article .
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.
Keywords: expert systems; verification and validation; knowledge acquisition; knowledge representation; rules; explanation; applications; uncertainty representation
Symposium: Expert systems. Introduction to expert systems
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.
Review of A Practical Guide to Designing Expert 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)
DEVELOPMENT OF EXPERT SYSTEMS METHODOLOGIES AND APPLICATIONS
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.
Expert systems: perils and promise
Communications of the ACM, 1986
Based on a review of some actual expert-system projects, guidelines are proposed for choosing appropriate applications and managing the development process.
Expert system methodologies and applications - a decade review from 1995 to 2004
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.
Artificial Intelligence And Expert Systems
The book is aimed at bringing out a comprehensive presentation of Artificial Intelligence (AI) based methodologies and software tools wherein, for the first time, the focus is on addressing a wide spectrum of problems in engineering.
DEVELOPMENT OF EXPERT SYSTEMS METHODOLOGIES AND APPLICATION
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.
2012
In this paper we will discuss a survey on various work done in different areas using Expert System(ES). Different methods and algorithms are used in different areas to solve the problem. An expert system is created to solve problems in a particular narrow domain of expertise. There are two different ways developers look at application areas for ES i.e. first, functional nature of problem and secondly, the application domain. One of the key characteristics of an ES is the explanation facility. With this capability, an ES can explain how it arrives at its conclusion. This survey is aimed at reviewing the recent scientific aspects of various research done on ES using different techniques like Case Based Reasoning(CBR), Rule-Based, Fuzzy Logic etc. Keywords— Expert System, Case Based Reasoning, CLIPS,