7 edition of Automatic refinement of expert system knowledge bases found in the catalog.
Includes bibliographical references (p. 173-176).
|Series||Research notes in artificial intelligence,, Research notes in artificial intelligence (London, England)|
|LC Classifications||QA76.76.E95 G55 1988|
|The Physical Object|
|Pagination||176 p. :|
|Number of Pages||176|
|ISBN 10||0273087940, 0934613966|
|LC Control Number||88012878|
Refinement tools assist with debugging the knowledge-based system (KBS), thus easing the well-known knowledge acquisition bottleneck, and the more recently recognised maintenance overhead. The existing refinement tools were developed for specific rule-based KBS environments, and have usually been applied to artificial or academic : Robin Boswell and Susan Craw. A knowledge-based system (KBS) is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. Ideal for advanced-undergraduate and graduate students, as well as business professionals, this text is designed to help users develop an appreciation of KBS and their architecture and understand a broad variety of 5/5(1).
Knowledge base refinement is the modification of an existing expert system knowledge base with the goals of localizing specific weaknesses in a knowledge base and improving an expert system's performarice. Systems that automate some aspects of knowledge base refinement canFile Size: 1MB. develop knowledge-based agents for complex military tasks such as course of action critiquing and center of gravity analysis (Tecuci et al. ). In this paper we present a new integrated approach to support a domain expert in refining the rules from an agent’s large knowledge base. Rule Learning and Refinement.
Knowledge base refinement is the modification of an existing expert system knowledge base with the goals of localizing specific weaknesses in a knowledge base and improving an expert system's performance. Systems that automate some aspects of knowledge base refinement can. In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. 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. The first expert systems were created in the s and then proliferated in the s.
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Knowledge Base Expert System Heuristic Rule Mixed Connective Tissue Disease Rule Refinement 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 by: 3. Refinement of existing knowledge bases is a subproblem of the knowledge-acquisition problem.
The paper presents a HEuristic REfinement System (HERES), which refines rules with mixed fuzzy and nonfuzzy concepts represented in a variant of the rule representation language Z-II by: 3.
Recent progress in knowledge base refinement for expert systems is reviewed. Knowledge base refinement is characterized by the constrained modification of rule-components in an existing knowledge.
Quality of knowledge bases is one of the most important requirements for acceptance and reliability of expert systems. We discuss a knowledge base refinement technique performing static and dynamic analyses of rule bases and present a deductive approach to justify refinement operations on rule : H.
Schimpe, M. Staudt, B. Kauert, A. Sperber. An automated approach to knowledge base refinement, an important aspect of knowledge acquisition is described. Using empirical performance analysis, S Cited by: This article describes a comprehensive system for automatic theory (knowledge base) refinement.
The system applies to classification tasks employing a propositional Horn-clause domain theory. the knowledge base (KB) and the process is known as KB refinement.
This approach has been considered by several authors, which have developed automatic KB refinement tools. These works have been mainly focused on developing learning strategies to improve the KB, but the validity of the refined ES has not been considered in detail.
knowledge base for our system is be built on existing searching practice. Current knowledge on good search technique is presented in Section Expert Systems CANSEARCH (Pollitt, ; Pollitt, ) is one of the earliest expert systems for bibliographic retrieval. It is designed to enable doctors to search the MEDLINE.
deep knowledge automatic knowledge base refinement detailed hierarchical description domain theory fast reasoning primary medical care field empirical learning technique knowledge base second generation expert system disease description learned concept machine learning technique general disease description system mesicar-learn implement.
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper describes an approach to knowledge base refinement, an important aspect of knowledge acquisition.
Knowledge base refinement is characterized by the addition, deletion, and alteration of rule-components in an existing knowledge base, in an attempt to improve an expert system's performance.
Automatic Knowledge Base Refinement: Learning from Examples and Deep Knowledge in Rheumatology. By In Rheumatology, MESICAR is a second generation expert system which contains very general disease descriptions about rheumatological disorders in the primary medical care field.
With the help of a detailed hierarchical description of the. Traditional methods for semi-automatic refinement of the knowledge base of an expert system for heuristic classifi- cation problems [Clancey, have centered around in- duction over a case library of examples.
Well-known sys terns that demonstrate this approach include ID3 [Quin. its “domain of expertise.” Knowledge base refinement may thus be viewed as being a part of, or a well-constrained subcase of, the knowledge acquisition problem*, Recently a variety of methods or approaches to rule refinement for expert system knowledge bases have been presented [3, In.
ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS: KNOWLEDGE-BASED SYSTEMS TEACHING SUGGESTIONS The introduction of artificial intelligence concepts can seem overwhelming to some students.
This is an excellent opportunity to utilize highly-involved, hands-on teaching techniques. Carefully review the group exercises on page 8 at the end of this chapter. Although the title of the book mentions the phrase ‘Expert Systems’, the book is in reality about knowledge (based) systems.
Nevertheless, it is assumed that such systems are built using human knowledge only. Since the beginning of the s there has been considerable emphasis on using machine learning methods to build such systems Cited by: Recent progress in knowledge base refinement for expert systems is reviewed.
Knowledge base refinement is characterized by the constrained modification of rule-components in an existing knowledge base. The goals are to localize specific weaknesses in a knowledge base and to improve an expert system's by: 3.
Bibliographic content of Knowledge Based Systems, Volume 4. default search action. combined dblp search; author search; Integrating expert systems and decision-support systems: principles and practice. Automatic refinement of knowledge bases with fuzzy rules.
Book. An "expert system" is a knowledge-based computer system that emulates the decision-making ability of a human expert. The primary role of the expert system is to perform appropriate functions under the close supervision of the human, whose work is supported by that expert system.
In the reverse, this same expert system can monitor and double. Applying Expert System Technology to Business/Book and Disk by Lyons, Automatic Refinement of Expert System Knowledge Bases (Research Notes in Artificial Intelligence) Verifying and Validating Personal Computer-Based Expert Systems.
A. Terry Bahill. Published by Prentice Hall.Home Browse by Title Proceedings AAAI'86 A metalinguistic approach to the construction of knowledge base refinement systems.
ARTICLE. A metalinguistic approach to the construction of knowledge base refinement systems. Share on. Author: Allen Ginsberg.MESICAR is a second generation expert system which contains very general descriptions of rheumatological disorders in the primary medical care field.
With the help of a detailed hierarchical description of the human anatomy the system is able to support diagnostic decisions. The paper describes how .