Name
Abstract | ||
|---|---|---|
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 prognosisRepertory GridsThe importance of symbolic and qualitative reasoningThe acquisition of fuzzy rulesThe best learning paradigm or combination of paradigmsImpact of machine learning on explanation systemsThe role of toy domains such as chessAutomatic programming revisitedApplications to computer vision, decision support systems, diagnosis, helpdesks, optimization, planning, scheduling, et al.Implementation issues using SIMD and MIMD platformsSources for joint sponsorshipForming industrial partnershipsForming alliances abroad |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1145/170791.171158 | ACM Conference on Computer Science |
Keywords | DocType | ISBN |
small-scale knowledge base,expert system,knowledge acquisition process,knowledge acquisition bottleneck,knowledge base,expert systems technology,knowledge acquisition,domain expert,so-called knowledge acquisition bottleneck,generation expert system | Conference | 0-89791-558-5 |
Citations | PageRank | References |
1 | 0.38 | 4 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Fogel | 1 | 1 | 0.38 |
| John C. Hanson | 2 | 3 | 0.79 |
| Russell C. Kick | 3 | 3 | 1.47 |
| Heidar A. Malki | 4 | 123 | 14.79 |
| Charles D. Sigwart | 5 | 9 | 2.89 |
| Michael Stinson | 6 | 11 | 3.21 |
| Efraim Turban | 7 | 1113 | 80.25 |
| Stuart H. Rubin | 8 | 199 | 31.06 |