Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Diagnosability testing with satisfiability algorithms

Thumbnail Image

Date

2007

Authors

Rintanen, Jussi
Grastien, Alban

Journal Title

Journal ISSN

Volume Title

Publisher

Carnegie Mellon University

Abstract

We show how testing whether a system is diagnosable can be reduced to the satisfiability problem and how satisfiability algorithms yield a very efficient approach to testing diagnosability. Diagnosability is the question whether it is always possible to k

Description

Keywords

Keywords: AI planning; Diagnosability; Failure behaviors; Finding paths; Satisfiability algorithms; Satisfiability problems; System behaviors; Transition graphs; Algorithms; Artificial intelligence; Formal logic

Citation

URI

http://hdl.handle.net/1885/29605

Collections

ANU Research Publications

Source

Proceedings of the International Joint Conference on Artificial Intelligence

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

Restricted until

2037-12-31

Downloads

File
Description
03_Rintanen_Diagnosability_testing_with_2007.pdf (308.01 KB)

DSpace software copyright © 2002-2026 LYRASIS

abcd