Name
Abstract | ||
|---|---|---|
This paper presents a novel approach to diagnosis which addresses the two problems - computational complexity of abduction and device models - that have prevented model-based diagnostic techniques from be- ing widely used. The Experience-Aided Diagnosis (EAD) model is defined that combines deduction to rule out hypotheses, abduction to generate hypotheses and induction to recall past experiences and account for potential errors in the device models. A detailed analysis of the relationship between case-based reason- ing and induction is also provided. The EAD model yields a practical method for solving hard diagnostic problems and provides a theoretical basis for overcom- ing the problem of partially incorrect device models. |
Year | Venue | Keywords |
|---|---|---|
1994 | AAAI | complex device,experience-aided diagnosis,case base reasoning,computational complexity |
Field | DocType | ISBN |
Aided diagnosis,Computer science,Artificial intelligence,Recall,Machine learning,Computational complexity theory | Conference | 0-262-61102-3 |
Citations | PageRank | References |
5 | 0.64 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michel P. Féret | 1 | 10 | 1.91 |
| Janice I. Glasgow | 2 | 392 | 127.97 |