Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Sixth Conference on Artificial Intelligence for Applications |
| Seiten | 206-213 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 1990 |
| Extern publiziert | Ja |
| Veranstaltung | Sixth Conference on Artificial Intelligence for Applications - Santa Barbara, USA / Vereinigte Staaten Dauer: 5 Mai 1990 → 9 Mai 1990 |
Abstract
It is shown how to implement the core of a model-based diagnosis system by a small hyperresolution-based procedure using Prolog. The algorithm is able to find all possible diagnosis candidates. The model-based diagnosis by model generation (MOMO) algorithm has well-defined semantics and allows the description of models using general range-restricted clauses, in contrast to earlier systems, which only allow a Horn clause description. A large class of systems can be modeled and can incorporate different types of behavioral models, such as correct behavior models, alibis, and physical necessity rules. As the basic algorithm can be easily implemented by a few Prolog clauses, it can serve as a testbed for various ideas concerning model-based diagnosis without using a full-fledged environment incorporating assumption-based truth maintenance system (ATMS) techniques. The algorithm has been tested using several models including well-known example systems as well as various modeling assumptions.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
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- RIS
Sixth Conference on Artificial Intelligence for Applications. 1990. S. 206-213.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - MOMO--Model-based diagnosis for everybody
AU - Friedrich, Gerhard
AU - Nejdl, Wolfgang
PY - 1990
Y1 - 1990
N2 - It is shown how to implement the core of a model-based diagnosis system by a small hyperresolution-based procedure using Prolog. The algorithm is able to find all possible diagnosis candidates. The model-based diagnosis by model generation (MOMO) algorithm has well-defined semantics and allows the description of models using general range-restricted clauses, in contrast to earlier systems, which only allow a Horn clause description. A large class of systems can be modeled and can incorporate different types of behavioral models, such as correct behavior models, alibis, and physical necessity rules. As the basic algorithm can be easily implemented by a few Prolog clauses, it can serve as a testbed for various ideas concerning model-based diagnosis without using a full-fledged environment incorporating assumption-based truth maintenance system (ATMS) techniques. The algorithm has been tested using several models including well-known example systems as well as various modeling assumptions.
AB - It is shown how to implement the core of a model-based diagnosis system by a small hyperresolution-based procedure using Prolog. The algorithm is able to find all possible diagnosis candidates. The model-based diagnosis by model generation (MOMO) algorithm has well-defined semantics and allows the description of models using general range-restricted clauses, in contrast to earlier systems, which only allow a Horn clause description. A large class of systems can be modeled and can incorporate different types of behavioral models, such as correct behavior models, alibis, and physical necessity rules. As the basic algorithm can be easily implemented by a few Prolog clauses, it can serve as a testbed for various ideas concerning model-based diagnosis without using a full-fledged environment incorporating assumption-based truth maintenance system (ATMS) techniques. The algorithm has been tested using several models including well-known example systems as well as various modeling assumptions.
UR - http://www.scopus.com/inward/record.url?scp=0025568284&partnerID=8YFLogxK
U2 - 10.1109/CAIA.1990.89191
DO - 10.1109/CAIA.1990.89191
M3 - Conference contribution
AN - SCOPUS:0025568284
SN - 0-8186-2032-3
SP - 206
EP - 213
BT - Sixth Conference on Artificial Intelligence for Applications
T2 - Sixth Conference on Artificial Intelligence for Applications
Y2 - 5 May 1990 through 9 May 1990
ER -