Name
Title | ||
|---|---|---|
Contextualized Possibilistic Networks with Temporal Framework for Knowledge Base Reliability Improvement |
Abstract | ||
|---|---|---|
Possibilistic abductive reasoning is particularly suited for diagnostic problem solving affected by uncertainty. Being a Knowledge-Based approach, it requires a Knowledge Base consisting in a map of causal dependencies between failures (or anomalies) and their effects (symptoms). Possibilistic Causal Networks are an effective formalism for knowledge representation within this applicative field, but are affected by different issues. This paper is focused on the importance of a proper management of explicit contextual information and of the addition of a temporal framework to traditional Possibilistic Causal Networks for the improvement of diagnostic process performances. The necessary modifications to the knowledge representation formalism and to the learning approach are presented together with a brief description of an applicative test case for the concepts here discussed. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73400-0_6 | WILF |
Keywords | Field | DocType |
possibilistic causal networks,knowledge representation,applicative field,effective formalism,applicative test case,contextualized possibilistic networks,traditional possibilistic causal networks,diagnostic process performance,possibilistic abductive reasoning,temporal framework,knowledge-based approach,diagnostic problem,knowledge base reliability improvement,knowledge base,abductive reasoning | Contextual information,Knowledge representation and reasoning,Computer science,Abductive reasoning,Artificial intelligence,Formalism (philosophy),Knowledge base,Possibility distribution,Machine learning | Conference |
Volume | ISSN | Citations |
4578 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Grasso | 1 | 18 | 1.65 |
| Michèle Lavagna | 2 | 2 | 1.79 |
| Guido Sangiovanni | 3 | 3 | 2.86 |