Knowledge-based Expert Systems : Principles and Practice
Identifieur interne : 003E87 ( Crin/Checkpoint ); précédent : 003E86; suivant : 003E88Knowledge-based Expert Systems : Principles and Practice
Auteurs : Jean-Paul Haton [France]Source :
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Abstract
Knowledge-based techniques have lead to important applications in various fields of Artificial Intelligence, especially expert systems (ES). ES are computer programs which emulate the performances of human experts for different tasks\, : diagnosis, data interpretation, decision making that are encountered in knowledge-intensive domains like medicine, engineering, bank and finance, etc. This paper will first briefly describe the basic principles of an ES, i.e. the reasoning process of an inference engine which manipulates a knowledge base. The main advantages of ES methodology will be pointed out, particularly the possibility of incrementally building up a complex system and of updating and maintaining a knowledge base, and the capability for an ES to explain its reasoning process. The major drawbacks and present limitations will also be presented as well as the ongoing research effort towards a new generation of ES. We will then discuss the various issues involved in the development of an ES in a particular domain, especially in relation with the acquisition of knowledge from the expert and with its representation within the system (various representation paradigms will be presented, including production rules, frames and objects). The presentation will be illustrated by practical examples of ES from different fields including medicine and we will conclude on the foreseable future of ES.
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="83">Knowledge-based Expert Systems : Principles and Practice</title></titleStmt><publicationStmt><idno type="RBID">CRIN:haton88b</idno><date when="1988" year="1988">1988</date><idno type="wicri:Area/Crin/Corpus">000636</idno><idno type="wicri:Area/Crin/Curation">000636</idno><idno type="wicri:explorRef" wicri:stream="Crin" wicri:step="Curation">000636</idno><idno type="wicri:Area/Crin/Checkpoint">003E87</idno><idno type="wicri:explorRef" wicri:stream="Crin" wicri:step="Checkpoint">003E87</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Knowledge-based Expert Systems : Principles and Practice</title><author><name sortKey="Haton, J P" sort="Haton, J P" uniqKey="Haton J" first="J.-P." last="Haton">Jean-Paul Haton</name><affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="region" nuts="2">Lorraine (région)</region></placeName><orgName type="laboratoire" n="5">Laboratoire lorrain de recherche en informatique et ses applications</orgName><orgName type="university">Université de Lorraine</orgName><orgName type="institution">Centre national de la recherche scientifique</orgName><orgName type="institution">Institut national de recherche en informatique et en automatique</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>artificial intelligence</term><term>expert systems</term><term>knowledge-based systems</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="4825">Knowledge-based techniques have lead to important applications in various fields of Artificial Intelligence, especially expert systems (ES). ES are computer programs which emulate the performances of human experts for different tasks\, : diagnosis, data interpretation, decision making that are encountered in knowledge-intensive domains like medicine, engineering, bank and finance, etc. This paper will first briefly describe the basic principles of an ES, i.e. the reasoning process of an inference engine which manipulates a knowledge base. The main advantages of ES methodology will be pointed out, particularly the possibility of incrementally building up a complex system and of updating and maintaining a knowledge base, and the capability for an ES to explain its reasoning process. The major drawbacks and present limitations will also be presented as well as the ongoing research effort towards a new generation of ES. We will then discuss the various issues involved in the development of an ES in a particular domain, especially in relation with the acquisition of knowledge from the expert and with its representation within the system (various representation paradigms will be presented, including production rules, frames and objects). The presentation will be illustrated by practical examples of ES from different fields including medicine and we will conclude on the foreseable future of ES.</div></front></TEI><BibTex type="inproceedings"><ref>haton88b</ref><crinnumber>88-R-109</crinnumber><category>6</category><equipe>INCONNUE</equipe><author><e>Haton, J.-P.</e></author><title>Knowledge-based Expert Systems : Principles and Practice</title><booktitle>{Actes CARDIO-STIM 88, Monte-Carlo}</booktitle><year>1988</year><month>jun</month><keywords><e>knowledge-based systems</e><e>expert systems</e><e>artificial intelligence</e></keywords><abstract>Knowledge-based techniques have lead to important applications in various fields of Artificial Intelligence, especially expert systems (ES). ES are computer programs which emulate the performances of human experts for different tasks\, : diagnosis, data interpretation, decision making that are encountered in knowledge-intensive domains like medicine, engineering, bank and finance, etc. This paper will first briefly describe the basic principles of an ES, i.e. the reasoning process of an inference engine which manipulates a knowledge base. The main advantages of ES methodology will be pointed out, particularly the possibility of incrementally building up a complex system and of updating and maintaining a knowledge base, and the capability for an ES to explain its reasoning process. The major drawbacks and present limitations will also be presented as well as the ongoing research effort towards a new generation of ES. We will then discuss the various issues involved in the development of an ES in a particular domain, especially in relation with the acquisition of knowledge from the expert and with its representation within the system (various representation paradigms will be presented, including production rules, frames and objects). The presentation will be illustrated by practical examples of ES from different fields including medicine and we will conclude on the foreseable future of ES.</abstract></BibTex></record>Pour manipuler ce document sous Unix (Dilib)
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