Causal simulation and diagnosis of dynamic systems

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Abstract

Previous work in model-based reasoninga nd in reasoning about action and change has shown that causal knowledge is essential to perform proper inferences about discrete changes in a system modeled by a set of logical or qualitative constraints. In this work we show that causal information can also be conveniently used to greatly improve the efficiency of qualitative simulation, prunings purious behaviors and guiding the computation of the “successor” relation, yet maintainingt he ability to deal with ambiguous predictions. The advantages of the approach are demonstrated on test cases, including one from a real application, using a diagnostic engine based on a causaldirected constraint solver.

Lingua originaleInglese
Titolo della pubblicazione ospiteAIIA 2001
Sottotitolo della pubblicazione ospiteAdvances in Artificial Intelligence - 7th Congress of the Italian Association for Artificial Intelligence, Proceedings
EditorFloriana Esposito
EditoreSpringer Verlag
Pagine135-146
Numero di pagine12
ISBN (stampa)3540426019, 9783540426011
DOI
Stato di pubblicazionePubblicato - 2001
Evento7th Congress of the Italian Association for Artificial Intelligence, AIIA 2001 - Bari, Italy
Durata: 25 set 200128 set 2001

Serie di pubblicazioni

NomeLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2175
ISSN (stampa)0302-9743
ISSN (elettronico)1611-3349

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???event.eventtypes.event.conference???7th Congress of the Italian Association for Artificial Intelligence, AIIA 2001
Paese/TerritorioItaly
CittàBari
Periodo25/09/0128/09/01

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