article

Production rules as a representation for a knowledge-based consultation program

Authors:

Randall Davis,

Bruce Buchanan,

Edward ShortliffeAuthors Info & Claims

Artificial Intelligence, Volume 8, Issue 1

Pages 15 - 45

https://doi.org/10.1016/0004-3702(77)90003-0

Published: 01 February 1977 Publication History

Abstract

The MYCIN system has begun to exhibit a high level of performance as a consultant on the difficult task of selecting antibiotic therapy for bacteremia. This report discusses issues of representation and design for the system. We describe the basic task and document the constraints involved in the use of a program as a consultant. The control structure and knowledge representation of the system are examined in this light, and special attention is given to the impact of production rules as a representation. The extent of the domain independence of the approach is also examined.

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Göndöcs DDörfler V(2024)AI in medical diagnosisArtificial Intelligence in Medicine10.1016/j.artmed.2024.102769149:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102769
Zhao AGu ZJia YFeng WYang JZhang Y(2023)TSEE: a novel knowledge embedding framework for cyberspace securityWorld Wide Web10.1007/s11280-023-01220-926:6(4131-4152)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s11280-023-01220-9
Dazeley RVamplew PCruz F(2023)Explainable reinforcement learning for broad-XAI: a conceptual framework and surveyNeural Computing and Applications10.1007/s00521-023-08423-135:23(16893-16916)Online publication date: 6-Mar-2023
https://dl.acm.org/doi/10.1007/s00521-023-08423-1
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Production rules as a representation for a knowledge-based consultation program
1. Theory of computation

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Published In

Artificial Intelligence Volume 8, Issue 1

February 1977

129 pages

ISSN:0004-3702

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Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 February 1977

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Göndöcs DDörfler V(2024)AI in medical diagnosisArtificial Intelligence in Medicine10.1016/j.artmed.2024.102769149:COnline publication date: 1-Mar-2024
Zhao AGu ZJia YFeng WYang JZhang Y(2023)TSEE: a novel knowledge embedding framework for cyberspace securityWorld Wide Web10.1007/s11280-023-01220-926:6(4131-4152)Online publication date: 1-Nov-2023
Dazeley RVamplew PCruz F(2023)Explainable reinforcement learning for broad-XAI: a conceptual framework and surveyNeural Computing and Applications10.1007/s00521-023-08423-135:23(16893-16916)Online publication date: 6-Mar-2023
Kaur HAdar EGilbert ELampe C(2022)Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking TheoryProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533135(702-714)Online publication date: 21-Jun-2022
Gutierrez CSequeda J(2021)Knowledge graphsCommunications of the ACM10.1145/341829464:3(96-104)Online publication date: 22-Feb-2021
Araujo Neto JPianto DRalha C(2019)MULTSJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2019.101651101:COnline publication date: 1-Dec-2019
Lima TSantana RTeodoro MNobre C(2019)Knowledge Extraction from Vector Machine Support in the Context of Depression in Children and AdolescentsProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-33904-3_51(545-555)Online publication date: 28-Oct-2019
Abdul AVermeulen JWang DLim BKankanhalli MMandryk RHancock MPerry MCox A(2018)Trends and Trajectories for Explainable, Accountable and Intelligible SystemsProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3174156(1-18)Online publication date: 21-Apr-2018
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Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system

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