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Expert systems: working systems and the research literature
Bruce G. Buchanan
- pp 34-75
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TLDR
In this article, the authors present a list of sixty expert systems that have moved out of development laboratories into field test and routine use, and about sixty such systems are listed as examples.Abstract:
Many expert systems have moved out of development laboratories into field test and routine use. About sixty such systems are listed. Academic research laboratories are contributing manpower to fuel the commercial development of AI. But the quantity of AI research may decline as a result unless the applied systems are experimented with and analyzed.read more
Citations
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Journal ArticleDOI
A philosophical basis for knowledge acquisition
TL;DR: It is suggested that the “insight” hypothesis of Lonergan (1958) better explains the flexibility and relativity of knowledge that the knowledge engineer experiences and may provide a more suitable philosophical environment for developing knowledge acquisition and representation tools.
Journal ArticleDOI
Artificial Intelligence in Medical Diagnosis
TL;DR: Advances have been developed to limit the number of hypotheses that a program must consider and to incorporate pathophysiologic reasoning, which permits a program to analyze cases in which one disorder influences the presentation of another.
Book
Prolog Programming in Depth
TL;DR: Prolog as its own Metalanguage, Artificial Intelligence and the Search for Solutions, and Natural Language Processing.
Journal ArticleDOI
Early expert systems: where are they now?
TL;DR: An investigation exploring how the first wave of commercial expert systems, built during the early and mid-1980s, fared over time, shows that most of these systems fell into disuse or were abandoned during a five-year period from 1987 to 1992, while about a third continued to thrive.
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First generation expert systems: a review of knowledge acquisition methodologies
TL;DR: A wide range of knowledge acquisition techniques are reviewed in the context of attempts to achieve a systematic methodology, and recognition of the need to create a conceptual model at the knowledge level (rather than the symbol level) is an important advance.
References
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Expert and novice performance in solving physics problems
TL;DR: Although a sizable body of knowledge is prerequisite to expert skill, that knowledge must be indexed by large numbers of patterns that, on recognition, guide the expert in a fraction of a second to relevant parts of the knowledge store.
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Diagnostic Models for Procedural Bugs in Basic Mathematical Skills
TL;DR: A new diagnostic modeling system for automatically synthesizing a deep-structure model of a student's misconceptions or bugs in his basic mathematical skills provides a mechanism for explaining why a student is making a mistake as opposed to simply identifying the mistake.
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R1: a rule-based configurer of computer systems
TL;DR: R1 is a program that configures VAX-11/780 computer systems and uses Match as its principal problem solving method; it has sufficient knowledge of the configuration domain and of the peculiarities of the various configuration constraints that at each step in the configuration process, it simply recognizes what to do.
Diagnostic Reasoning Based on Structure and Behavior
TL;DR: A system that reasons from first principles, i.e., using knowledge of structure and behavior, which is implemented and tested on several examples in the domain of troubleshooting digital electronic circuits and describes a technique it calls constraint suspension that provides a powerful tool for troubleshooting.