Adrian A. Hopgood · IGI Global eBooks · 2009
The tools of artificial intelligence (AI) can be divided into two broad types: knowledge-based systems (KBSs) and computational intelligence (CI). KBSs use explicit representations of knowledge in the form of words and symbols. This explicit representation makes the knowledge more easily read and understood by a human than the numerically derived implicit models in computational intelligence. KBSs include techniques such as rule-based, modelbased, and case-based reasoning. They were among the first forms of investigation into AI and remain a major theme. Early research focused on specialist applications in areas such as chemistry, medicine, and computer hardware. These early successes generated great optimism in AI, but more broad-based representations of human intelligence have remained difficult to achieve (Hopgood, 2003; Hopgood, 2005).
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2007
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Michie, Donald. · The Journal of the Abraham Lincoln Association
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Peter Szolovits · 1995
This paper will briefly review the origins and motivations for the field, indicate the considerable successes it has achieved, outline the many remaining difficulties, and highlight a few individual research results that point the way to its future development.
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J. Buckleton, Katherine Cramer Walsh · 1991
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Dietrich Hofmann · Handbook of Measuring System Design · 2005
Abstract An innovative paradigm in measurement engineering is the application of the knowledge‐based Web working between innovative enterprises, universities, and research organizations to support Internet metrology. The aim of the article is to discuss the new knowledge‐based methods, which are mature for application in everyday measurements and what they are able to do. A key feature is the new understanding concerning simple and complex measurements and the elaboration and standardization of not only metrological but also particular and objective virtual measuring standards. Practical examples for knowledge‐based legal metrology and knowledge‐based industrial metrology are given.
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Bruce G. Buchanan, Daniel G. Bobrow, Randall Davis et al. · Annual Review of Computer Science · 1990
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Akerkar, Rajendra, Sajja, Priti · CERN Document Server (European Organization for Nuclear Research) · 2010
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BLESSING, Lucienne · Open Repository and Bibliography (University of Luxembourg) · 1990
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Paul Beynon‐Davies · 1998
Knowledge based systems (KBS), sometimes referred to as intelligent knowledge based systems (IKBS), were much discussed in the literature during the 1980s. In the 1990s they have become an accepted tool in the information systems developer’s toolkit. This chapter attempts to explain exactly what is meant by a knowledge based system and makes some suggestions as to the place of knowledge based systems within information systems development.
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Nils J. Nilsson · Elsevier eBooks · 1998
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Klaus Mainzer · Journal for General Philosophy of Science · 1990
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Lieven Dubois, Philippe Mack · 2024
The knowledge-based system (KBS) (also commonly referred to as the expert system) approach to problem-solving is a programming paradigm that also originally evolved within the field of artificial intelligence. In reality, this approach encompasses a vast collection of diverse problem-solving strategies. The common characteristic uniting all of these various KBS strategies is that, when it comes to problem-solving, each unequivocally stresses the dominance of domain-specific knowledge over the particular problem-solving algorithm being employed. This programming strategy has found wide spread support because it has been successfully applied to solve a variety of extremely difficult problems in many diverse application domains which previously could only be solved by human experts. This chapter discusses the current state of KBS applications to process fault diagnosis.
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Haton, Jean-Paul, Centre National de la Recherche Scientifique (CNRS), 54 - Vandoeuvre-les-Nancy (France). Centre de Recherche en Informatique de Nancy (CRIN), Nancy-1 Univ., 54 - Vandoeuvre-les-Nancy (France). Centre de Recherche en Informatique de Nancy (CRIN) et al. · OpenGrey (Institut de l'Information Scientifique et Technique) · 1991
Paru dans : Actes symposium international intelligence naturelle et intelligence artificielle, Rome, septembre 1991
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Akerkar · CERN Document Server (European Organization for Nuclear Research) · 2010
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Encyclopedia of Environmental Change · 2014
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Randall Davis · Science · 1986
First developed two decades ago, knowledge-based systems have seen widespread application in recent years. While performance has been a strong focus of attention, building such systems has also expanded our conception of a computer program from a black box providing an answer to something capable of explaining its answers, acquiring new knowledge, and transferring knowledge to students. These abilities derive from distinguishing clearly what the program knows from how that knowledge will be used, making it possible to use the same knowledge in different ways.
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M. G. Kahn · Yearbook of Medical Informatics · 1993
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Susan Craw, D. Sleeman · 1995
This paper describes KRUST, an automated refinement system for knowledge based systems. Its aim is to fulfill an updating or maintenance role, rather than initial knowledge acquisition. Therefore, it concentrates on (re-)using available knowledge rather than relying on a large set training cases; i.e. faults to be fixed. We believe KRUST should be used to correct a knowledge base which requires tweaking rather than a major overhaul and we maintain that this approach is a suitable means to allow a knowledge base to evolve in order to keep up with a slowly changing domain. The expert provides the correct answer for a wrongly solved task and this task-solution pair is used as a training case for the refinement process. Our approach to knowledge refinement combines a search of possible refinements, with the implementation of repairs in the form of small changes available from additional knowledge hierarchies. KRUST is unusual in generating many refinements initially and then selectively re...
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Robert Gordon, Susan Craw, Susan Craw et al. · 1995
This paper describes KRUST, an automated refinement system for knowledge based systems. Its aim is to fulfill a maintenance, rather than acquisition, role and so it concentrates on (re-)using available knowledge, which in practice often does not include a large set of faults to be fixed. Our approach to knowledge refinement combines a best first search of possible refinements, with the implementation of repairs in the form of small changes available from additional knowledge hierarchies. The expert provides the correct answer for a wrongly solved task and this task-solution pair is used as a training case for the refinement process. KRUST is unusual in generating many refinements initially and then selectively removing those refinements or refined KBs that are shown to be ineffective, before finally recommending the change(s) which it deems to be best. As KRUST searches for suitable repairs, in addition to a static analysis of the declarative knowledge, it also takes account of the con...
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