Abstract
This paper presents an approach to model-based diagnosis that first compiles a first-order system description to a propositional representation, and then solves the diagnostic problem as a linear programming instance. Relevance reasoning is employed to isolate parts of the system that are related to certain observation types and to economically instantiate the theory, while methods from operations research offer promising results to generate near-optimal diagnoses efficiently.
| Original language | English |
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| Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
| Place of Publication | Menlo Park, CA, United States |
| Publisher | AAAI |
| Pages | 416-421 |
| Number of pages | 6 |
| ISBN (Print) | 0262511061 |
| Publication status | Published - 1999 |
| Externally published | Yes |
| Event | Proceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99) - Orlando, FL, USA Duration: 1999 Jul 18 → 1999 Jul 22 |
Other
| Other | Proceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99) |
|---|---|
| City | Orlando, FL, USA |
| Period | 99/7/18 → 99/7/22 |
ASJC Scopus subject areas
- Software