Name
Abstract | ||
|---|---|---|
We propose a technique to improve the performance of hierarchical model-based diagnosis, based on structural abstraction. Given a hierarchical representation and the set of currently available observations, the technique is able to dynamically derive a tailored hierarchical representation to diagnose the current situation. We implement our strategy as an extension to the well-known Mozetic's approach [Mozetic, 1992], and illustrate the obtained performance improvements. Our approach is more efficient than Mozetic's one when, due to abstraction, fewer observations are available at the coarsest hierarchical levels. |
Year | Venue | Keywords |
|---|---|---|
2001 | IJCAI | performance improvement,hierarchical model-based diagnosis,coarsest hierarchical level,hierarchical diagnosis,current situation,hierarchical representation,available observation,tailored hierarchical representation,fewer observation,well-known mozetic,structural abstraction |
Field | DocType | ISBN |
Abstraction,Computer science,Artificial intelligence,Hierarchical database model,Machine learning | Conference | 1-55860-812-5 |
Citations | PageRank | References |
2 | 0.48 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Chittaro | 1 | 2083 | 177.40 |
| Roberto Ranon | 2 | 392 | 33.19 |