A general characterization of Model-Based Diagnosis

CORA will be unavailable from 08:00- 09:00 for regular maintenance on Tuesday, 28th May 2019. Apologies for any inconvenience this may cause.

Files in this item

Icon
Name: 2618.pdf
Size: 294.0Kb
Format: PDF
Description: Published Version
View/Open

A general characterization of Model-Based Diagnosis

Provan, Gregory
Date: 2016-01
Copyright: © 2016, the Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0)
Copyright information: https://creativecommons.org/licenses/by-nc/4.0/
Related: http://www.ecai2016.org/
Citation: Provan, G. (2016) 'A general characterization of Model-Based Diagnosis' (from proceedings of the Twenty-second European Conference on Artificial Intelligence (ECAI 2016),The Hague, The Netherlands, 29th August - 2nd September), Frontiers in Artificial Intelligence and Applications, 285, pp. 1565-1566. doi:10.3233/978-1-61499-672-9-1565
Published Version: 10.3233/978-1-61499-672-9-1565

Abstract:

The Model-Based Diagnosis (MBD) framework developed by Reiter has been a strong theoretical foundation for MBD, yet is limited to models that are described in terms of logical sentences. We propose a more general framework that covers a wide range of modelling languages, ranging from AI-based languages (e.g., logic and Bayesian networks) to FDI-based languages (e.g., linear Gaussian models). We show that a graph-theoretic basis for decomposable system models can be augmented with several languages and corresponding inference algorithms based on valuation algebras.

Show full item record

This item appears in the following Collection(s)

© 2016, the Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0) Except where otherwise noted, this item's license is described as © 2016, the Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0)
This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement