The Logic Brain of the System: Combines rules to reach conclusions
Mathematical Models for Decision Support pp 487–517 Cite as
Expert Systems: The State of the Art
- M. J. Rijckaert 2 ,
- V. Debroey 2 &
- W. Bogaerts 2
- Conference paper
351 Accesses
3 Citations
Part of the NATO ASI Series book series (NATO ASI F,volume 48)
“An expert system is a computer program that embodies expertise about a particular domain, and can use symbolic reasoning techniques to solve problems in this domain; problems that would need the assistance of a human expert in the real world. An expert system should also be able to explain its conclusions.” This definition embraces the four main characteristics that distinguish an expert system from a “conventional” program (Waterman 1986):
Symbolic reasoning
Self-knowledge
- Expert System
- Human Expert
- Semantic Network
- Inference Engine
- Knowledge Engineer
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution .
Buying options
- Available as PDF
- Read on any device
- Instant download
- Own it forever
- Compact, lightweight edition
- Dispatched in 3 to 5 business days
- Free shipping worldwide - see info
Tax calculation will be finalised at checkout
Purchases are for personal use only
Unable to display preview. Download preview PDF.
J. L. Alty, M. J. Coombs.(1984) Expert Systems: Concepts and Examples . NCC Publications.
Google Scholar
F. Arlabosse (1985) Reasoning with time and about time: Some aspects of temporal logic. In: Expert Systems and Optimisation in Process Control , Unicom Seminars Ltd., 152–157.
J.F. Baldwin. (1987) An uncertainty calculus for expert systems. In: Approximate reasoning in intelligent systems, decision and control , ed. by E. Sanchez and L. Zadeh, Pergamon Press, 33–54.
D. C. Berry, D. E. Broadbent. (1987) Expert Systems and the man-machine interface. Part two: the user interface. Expert Systems , February 1987. Vol. 4 No. 1 18–27.
R. J. Brachman, S. Amarel, C. Engelman, R. S. Engelmore, E. A. Feigenbaum, D. E. Wilkins. (1983) What are Expert Systems?. Chapter 2 in ( Hayes-Roth 1983).
R. J. Brachman, H. J. Levesque. (1985) Readings in Knowledge Representation , Morgan Kaufmann.
L. Brownston, R. Farell, E. Kant, N. Martin. (1985) Programming Expert Systems in OPS5, Addison-Wesley.
B. G. Buchanan, D. Barstow, R. Bechtal, J. Bennett, W. Clancey, C. Kulowski, T. Mitchell, D. A. Waterman. (1983) Constructing an Expert System, Chapter 5 in (Hayes-Roth 1983).
B. G. Buchanan, E. H. Shortliffe. (1984) Rule-Based Expert Systems: the MYCIN Experiments of the Stanford Heuristic Programming Project , Addison-Wesley
T. Bylander, B. Chandrasekaran. (1985) Qualitative Reasoning about Physical Systems, In (S1GART 1985).
B. Chandrasekaran, S. Mittal. (1984) Deep versus compiled knowledge approaches to diagnostic problem-solving, In: Developments in Expert Systems , ed. M. J. Coombs, Academic Press, 23–34.
W. E. Clocksin, C. S. Mellish. (1981) Programming in Prolog , Springer-Verlag
R. Davis, B. Buchanan, E. Shortliffe. (1977) Production Rules as a Representation for a Knowledge-Based Consultation Program, Artificial Intelligence 8(1), 15–45.
CrossRef MATH Google Scholar
R. Davis. (1984) Resoning frum first principles, In: Developments in Expert Systems , ed. M. J. Coombs, Academic Press.
J. de Kleer, C. Williams. (1987) Diagnosing Multiple Faults, Artificial Intelligence 32, 97–130.
R. O. Duda, J. Gaschnig, P. Hart. (1979) Model design in the PROSPECTOR consultant program for mineral exploration, In: Expert Systems in tyhe Microelectronic Age , ed. D. Mitchie, Edinburgh University Press.
C. L. Forgy, J. Mc Dermott. (1977) OPS, A Domain-Independent Production System Language, In: Proceedings of the 5th International Joint Conference on Artificial Intelligence MIT , 973–939.
G. Guida. (1985) Reasoning about physical systems: shallow versus deep models, In: Expert Systems and Optimisation in Process Control , Unicom Seminars Ltd. 82–106.
F. Hayes-Roth, D. A. Waterman, D. B. Lenat. (1983) Building Expert Systems , Addison-Wesley.
F. Hayes-Roth. (1985) A Blackboard Architecture for Control, Artificial Intelligence 26, 251–321.
CrossRef Google Scholar
IntelliCorp. (1986a) KEE Software Development System User’s Manual, Intel-licorp Document number 3.0-U-1.
IntelliCorp. (1986b) KEE Software Development Rulesystem3 Reference Manual, Intellicorp Document number 3.0-RR-1.
J.L. Kolodner. (1984) Towards an understanding of the role od experience in the evolution from novice to expert, In: Developments in Expert Systems , ed. M. J. Coombs, Academic Press, 95–116.
D. Lenat, R. Davis, J. Doyle, M. Genesereth, I. Goldstein, H. Schrobe. (1983) Reasoning about Reasoning, Chapter 7 in (Hayes-Roth 1983).
J. McDermott. (1982) RI: A Rule-Based Configurer of Computer Systems, Artificial Intelligence 19, 39–88.
M. Minsky. (1985) A Framework for Representing Knowledge, In: Mind Design , ed. J. Haugeland, The MIT Press (1981) ( reprinted as Chapter 12 in ( Brachman (1985)).
R. L. Moore, L. B. Hawkinson, C. G. Knickerbocker, L. M. Churchman. (1984) A real-time expert system for process control, 1st Conf. on Artificial Intelligence Applications IEEE , 569–576.
L Moskowitz. (1986) Rule-Based Programming, Byte November, 217–224.
R. C. Muller (1986) New Directions in Expert Systems, In: Knowledge-Based Systems ’ 86, Online Publications, 325–333.
C.V. Negoita. (1985) Expert Systems and Fuzzy Systems , Benjamin Cummings Publ. Co.
G. Pass. (1986) Consistent Evaluation of Uncertain Reasoning Systems, 6th Int. Workshop on Expert Systems and their Applications, Avignon , 73–94.
S. Puuronen. (1987) A Tabular Rule-Checking Method, 7th Int. Workshop on Expert Systems and their Applications, Avignon , 257–268.
M. R. Quillian. (1967) Word Concepts: A Theory and Simulation of some Basic Semantic Capabilities, Behavioral Science 12, 410–430(reprinted as Chapter 6 in (Brachman (1985))).
J. R. Quinlan. (1982) Semi-automous acquisition of pattern-based knowledge, In: Introductory Readings in Expert Systems , ed D. Mitchie, Gordon and Breach.
M.-C. Rousset. (1987) Sur la Validite des Bases de Connaissances: le systeme COVADIS, 7th Int. Workshop on Expert Systems and their Applications, Avignon , 269–282.
V. Sembugamoorthy, B. Chandrasekaran. (1985) Functional Representation of Devices as Deep Models, In: (SIGART 1985).
G. Shafer. (1976) A mathematical theory of evidence , Princeton University Press.
E. H. Shortliffe. (1976) Computer-Based Medical Consultations: MYCIN , American Elsevier.
SIGART (1985) Special Section on Reasoning about structure, Behavior and Function, eds. B. Chandrasekaran, R. Milne, SIGART Newsletter July, 4–55.
G. Soula, X. Thirion, J.-M. Guliana, I. Navez, B. Vialettes, J.-L. San Marco, V. Ledoray. (1987) Une Validation Multicentrique du Systeme Expert PROTIS, 7th Int. Workshop on Expert Systems and their Applications, Avignon , 239–255.
J. T. St Johanser, R. M. Harbidge. (1986) Validating expert systems: problems and solutions in practice, In: Knowledge-Based Systems’ 86 , Online Publications, 215–229.
M. Suwa, A. C. Scott, E. H. Shortliffe. (1984) Completeness and Consistency in a Rule-Based System, In: (Buchanan 1984), 653–665.
S. N. Talukdar, E. Cardozo, L. Leao, R. Benares, R. Joobbani. (1986) A system for Distributed Problem Solving. In: Coupling Symbolic and Numerical Computing in Expert Systems , ed. J. S. Kowalik, Elsevier, 115–122.
D. A. Waterman (1986) A Guide to Expert Systems , Addison-Wesley.
Download references
Author information
Authors and affiliations.
Katholieke Universiteit Leuven, Belgium
M. J. Rijckaert, V. Debroey & W. Bogaerts
You can also search for this author in PubMed Google Scholar
Editor information
Editors and affiliations.
Department of Mathematics and Statistics Brunel, The University of West London, Uxbridge, Middlesex, UB8 3PH, UK
Gautam Mitra
Rights and permissions
Reprints and Permissions
Copyright information
© 1988 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper.
Rijckaert, M.J., Debroey, V., Bogaerts, W. (1988). Expert Systems: The State of the Art. In: Mitra, G., Greenberg, H.J., Lootsma, F.A., Rijkaert, M.J., Zimmermann, H.J. (eds) Mathematical Models for Decision Support. NATO ASI Series, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83555-1_29
Download citation
DOI : https://doi.org/10.1007/978-3-642-83555-1_29
Publisher Name : Springer, Berlin, Heidelberg
Print ISBN : 978-3-642-83557-5
Online ISBN : 978-3-642-83555-1
eBook Packages : Springer Book Archive
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
- Find a journal
- Publish with us
IMAGES
VIDEO
COMMENTS
Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. [2] The first expert systems were created in the 1970s and then proliferated in the 1980s. [3]
expert system, a computer program that uses artificial-intelligence methods to solve problems within a specialized domain that ordinarily requires human expertise.
knowledge and skills of human experts in some area, and then solve problems in this area (the way human experts would). † ES take their roots in Cognitive Science — the study of human mind using combination of AI and psychology. † ES were the first successful applications of AI to real-world problems solving problems in medicine ...
As a consequence, knowledge acquisition, the process of transferring knowledge from a source of expertise (either human or textual) to the expert system, is of paramount importance to the development of expert systems, but unfortunately it is also a major bottleneck in expert system design.
An expert system is a computer program that uses artificial intelligence ( AI) technologies to simulate the judgment and behavior of a human or an organization that has expertise and experience in a particular field. Expert systems are usually intended to complement, not replace, human experts.
What is an expert system? Definition 1 (Expert Systems). (ES) are computer programs that try to repli- cate knowledge and skills of human experts in some area, and then solve prob- lems in this area (the way human experts would). ES take their roots in Cognitive Science — the study of human mind using combination of AI and psychology.
Expert systems are computer programs that exhibit some of the characteristics of expertise in human problem solving, most notably high levels of performance. Several issues are described that are relevant for the study of expertise and that have arisen in the development of the technology.
3.2.1 Expert Systems. An expert system is a computer program designed to imitate a human expert, mimicking the knowledge base and the decision making process of a human expert. An expert system is different from conventional programs because it can explain its behavior to the human expert and receive new information without new programming.
Download chapter PDF. Expert System (ES) is a kind of software that simulates the problem-solving behavior of a human expert of given domain. ES can be used to solve a complex problem or give an advice, mainly in cases when the amount of data to be processed is very high. There is no sense to apply ESs for simple decision problems, they should ...
Abstract. A rule-based expert system is the most elementary form of AI, and it uses predetermined sets of steps to find an answer to a problem. The goal of an expert system is to encode the knowledge of a human expert as a set of rules that can be applied to data automatically. Download chapter PDF.
1. Introduction Expert systems are a branch of artificial intelligence that aim to provide computerized decision-making capabilities similar to those of a human expert in a specific domain. They are designed to solve complex problems using a set of rules or algorithms that can mimic human reasoning processes.
fected in an expert system by the use of three distinct components, as shown in Figure 2.2. The knowledge base constitutes the problem-solving rules, facts, or intui-tion that a human expert might use in solving problems in a given problem domain. The knowledge base is usually stored in terms of if-then rules. The
The problem-solving ability or "intelligence" of this expert system is extremely high in the narrow domain of scheduling planes to airport gates as indicated by its superior performance compared to that of undergraduates, graduate students and expert human schedulers (i.e. air traffic controllers).
An Expert System (ES) is a computer program that reasons using knowledge to solve complex problems. This overly brief caricature will be expanded upon below, but it serves to indicate an alignment of ES with AI's long term goals.
Chapter 1: I ntro d uct i on to Expert Systems Expert Systems: Principles and Programming, Fourth Edition Objectives Examine earlier expert systems which have given rise to today's knowledge-based systems Explore the applications of expert systems in use today Examine the structure of a rule-based expert system
intelligent expert systems. In addition to giving correct answers or useful advice in problem situations, intelligent expert systems use. concepts and reasoning processes that resemble those that the system. user might employ. A major problem in engineering such systems has. been in creating facilitieN that can give an explanatory account, in
Basically, it is a type of computer program that is used to simulate the judgement and behavior of humans or an organization that has an expert knowledge and experience about the particular field. Building of an expert system requires a human expert that extract the required knowledge. Figure - Expert System 2.
"An expert system is a computer program that embodies expertise about a particular domain, and can use symbolic reasoning techniques to solve problems in this domain; problems that would need the assistance of a human expert in the real world. An expert system should also be able to explain its conclusions." This definition embraces the four main characteristics that distinguish an expert ...
Introduction to Expert System. Expert System: A system which employs human expertise captured in a CBIS to solve problems which usually require human expertise. An expert system either supports or automates decision making in an area of which experts perform better than non experts. It is also known as "Expert Computing Systems", or "Knowledge ...
An expert system is AI software that uses knowledge stored in a knowledge base to solve problems that would usually require a human expert thus preserving a human expert's knowledge in its knowledge base. They can advise users as well as provide explanations to them about how they reached a particular conclusion or advice.
Expert System: An expert system is a computer program that is designed to emulate and mimic human intelligence, skills or behavior. It is mainly developed using artificial intelligence concepts, tools and technologies, and possesses expert knowledge in a particular field, topic or skill.
Share link "You are an expert in XYZ; explain its advanced concepts." This is an example of an "act as an expert" prompt commonly used in ChatGPT. In essence, the AI language model ChatGPT acts as an expert on the particular subject matter and responds similarly to a human expert.
Human Expert vs. Expert System. The significant distinction between expert systems in artificial intelligence and human experts is that expert systems process knowledge represented in the form of rules and use representational reasoning in a limited area, whereas human experts use knowledge in the form of heuristics of rules of thumb to solve problems in a limited domain.
Introducing Amazon Q. Transform your business with Amazon Q. Amazon Q is a generative AI assistant that can answer questions, provide summaries, generate content, and complete tasks based on data and information in your enterprise systems. This all happens through its web-based interface, so your employees can work smarter, move faster, and ...