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.
|Titolo:||Contextualized Possibilistic Networks with Temporal Framework for Knowledge Base Reliability Improvement|
|Autori interni:||GRASSO, MARCO LUIGI|
|Data di pubblicazione:||2007|
|Serie:||LECTURE NOTES IN ARTIFICIAL INTELLIGENCE|
|Appare nelle tipologie:||04.1 Contributo in Atti di convegno|