Expert systems are a well-known and well-received technology. It was thought that the performance of a domain expert could not be duplicated by a machine. Expert systems technologies have shown this to be a false belief, and indeed have demonstrated how experts themselves can come to depend on expert systems. Expert systems enjoy widespread use in industrial domains and further uses are planned. The growth in acceptance has been explosive since about 1986. Continued rampant growth appears to depend on cracking the so-called knowledge acquisition bottleneck.

The knowledge acquisition bottleneck limits the scalability of expert systems. While it is relatively straightforward to populate a small-scale knowledge base, it becomes more difficult to maintain consistency and validity as the knowledge base grows. Thus, it is important to automate the knowledge acquisition process. A by-product of this process is that any failure of the expert system will be “soft.”

The question is, “What impact can machine learning technologies have on knowledge acquisition in the large?” The true test will be on prospective industrial applications in areas such as biology, education, geology, medicine, and scientific discovery. Machine learning technologies include expert systems, genetic algorithms, neural networks, random seeded crystal learning, or any effective combinations.

Relevant subtopics include:

Second generation expert systems — progress and prognosis

Repertory Grids

The importance of symbolic and qualitative reasoning

The acquisition of fuzzy rules

The best learning paradigm or combination of paradigms

Impact of machine learning on explanation systems

The role of toy domains such as chess

Automatic programming revisited

Applications to computer vision, decision support systems, diagnosis, helpdesks, optimization, planning, scheduling, et al.

Implementation issues using SIMD and MIMD platforms

Sources for joint sponsorship

Forming industrial partnerships

Forming alliances abroad

References

[1]

J.W. Atmar, "Speculation on the Evolution of Intelligence and its Possible Realization in Machine Form," Doctoral dissertation, New Mexico State University, Las Cruces, NM, 1976.]]

Digital Library

Google Scholar

[2]

B.K. Belew and L.B. Booker (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kauffmann Publishers, San Mateo, CA, 199 1.]]

Crossref

Google Scholar

[3]

D.B. Fogel and W. Abnar (eds.) Proceedings ofrhe First Annual Conference on Evolutionary Programming, Evolutionary Programming Society, La Jolla, CA, 1992.]]

Google Scholar

[4]

R. M(\a)t'ttXX and H.-P. Schwefel (eds.) Proceedings of the First International Conference on Parallel Problem Solving from Nature (PPSN), Springer, Germany, 199 1.]]

Google Scholar

[5]

N. Weiner, Cybernefics, Part II, MIT Press, Cambridge, MA, 1961.]]

Google Scholar

[6]

Warren S. McCulloch and Walter Pitts "A logical Calculus of ideas immanent in nervous activity", Bull. Math. Biophysics 5: p.llS-133 (1943)]]

Google Scholar

[7]

Newell and Simon, Turing Award Lecture 1975 "Computer Science as Empirical Inquiry: Symbols and Search"]]

Digital Library

Google Scholar

[8]

Charles Rich and Y.A. Feldman "Seven Layers of Knowledge Representation and Reasoning in Support of Software Development Trans. Software Eng. SE-18(6): p. 45 1469 (June, 1992)]]

Digital Library

Google Scholar

[9]

David E. Rumelhart and James L. McClelland PARALLEL DISTRIBUTED PROCESSING, MIT Press, 1986]]

Digital Library

Google Scholar

[10]

EricShoen,etal"Designof KnowledgeBased Systems withaKnowledge Based AssistarVTrans. SoftwareEng. SE-14(12): p.1771-1791(Dec. 1988)]]

Digital Library

Google Scholar

[11]

Barry G. Silverman, "Survey of Expert Critiquing Systems: Practical and Theoretical Frontiers" CACM 35(4); p. 106-127 (April 1992)]]

Digital Library

Google Scholar

[12]

RichardC. Waters,"TheProgrammers Apprentice: Asession withKBEmacs"IEEETransSoftwareEng. SE-l l(1 l);p.1296- 132O,(Nov. 1985)]]

Digital Library

Google Scholar

[13]

Maurice Wilkes, "Artificial Intelligence as the year 2000 approaches" CACM 35, p-17-20,(Aug.1992)]]

Digital Library

Google Scholar

Cited By

View all

Weitz K(2023)Erklärbare Künstliche Intelligenz im Kontext von Bildung und LernenKünstliche Intelligenz in der Bildung10.1007/978-3-658-40079-8_20(413-432)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-3-658-40079-8_20
Wang HChang CLi T(2019)Assessing creative problem-solving with automated text gradingComputers & Education10.1016/j.compedu.2008.01.00651:4(1450-1466)Online publication date: 22-Nov-2019
https://dl.acm.org/doi/10.1016/j.compedu.2008.01.006
Noran O(2008)A Decision Support System for Enterprise EngineeringInformation Systems Development10.1007/978-0-387-68772-8_41(533-546)Online publication date: 14-Sep-2008
https://doi.org/10.1007/978-0-387-68772-8_41
Show More Cited By

Index Terms

The impact of machine learning on expert systems
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning settings
    2. Machine learning approaches
      1. Neural networks
      2. Rule learning
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

Recommendations

Requirements for machine learning in expert systems (abstract)
CSC '90: Proceedings of the 1990 ACM annual conference on Cooperation

As Dietterich pointed out at the 1989 Machine Learning Workshop, the field needs analyses of the kinds of tasks that require machine learning (ML), and of the suitability of existing ML techniques for solving real problems. An area often mentioned as ...
Inexact reasoning in an inductive learning environment (expert systems, uncertainty)
Cognitive Expert Systems and Machine Learning: Artificial Intelligence Research at the University of Connecticut

In order for next‐generation expert systems to demonstrate the performance, robustness, flexibility, and learning ability of human experts, they will have to be based on cognitive models of expert human reasoning and learning We call such next‐generation ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

CSC '93: Proceedings of the 1993 ACM conference on Computer science

March 1993

543 pages

ISBN:0897915585

DOI:10.1145/170791

Chairmen:
Stan C. Kwasny
Washington Univ., St. Louis, MO
,
John F. Buck
Indiana Univ., Indianapolis

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 1993

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

CSC93

Sponsor:

CSC93: 21st Annual ACM Computer Science Conference

February 16 - 18, 1993

Indiana, Indianapolis, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
1,176
Total Downloads

Downloads (Last 12 months)129
Downloads (Last 6 weeks)16

Reflects downloads up to 14 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Weitz K(2023)Erklärbare Künstliche Intelligenz im Kontext von Bildung und LernenKünstliche Intelligenz in der Bildung10.1007/978-3-658-40079-8_20(413-432)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-3-658-40079-8_20
Wang HChang CLi T(2019)Assessing creative problem-solving with automated text gradingComputers & Education10.1016/j.compedu.2008.01.00651:4(1450-1466)Online publication date: 22-Nov-2019
https://dl.acm.org/doi/10.1016/j.compedu.2008.01.006
Noran O(2008)A Decision Support System for Enterprise EngineeringInformation Systems Development10.1007/978-0-387-68772-8_41(533-546)Online publication date: 14-Sep-2008
https://doi.org/10.1007/978-0-387-68772-8_41
Rubin SKennedy L(1994)An RSCL3 tutor for debugging Pascal programsProceedings of IEEE International Conference on Systems, Man and Cybernetics10.1109/ICSMC.1994.400246(2505-2510)Online publication date: 1994
https://doi.org/10.1109/ICSMC.1994.400246
Rubin S(1994)The future of random-seeded crystal learning in computer-aided instructionProceedings of 1994 IEEE Frontiers in Education Conference - FIE '9410.1109/FIE.1994.580607(579-582)Online publication date: 1994
https://doi.org/10.1109/FIE.1994.580607

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Requirements for machine learning in expert systems (abstract)

Inexact reasoning in an inductive learning environment (expert systems, uncertainty)

Cognitive Expert Systems and Machine Learning: Artificial Intelligence Research at the University of Connecticut