Abstract
In this paper we concentrate on practicalasp ects of qualitative modeling and reasoning about physical systems, reporting our experience within the VMBD project1 in applying Constraint Programming techniques to the task of diagnosing a real-life automotive subsystem. We propose a layered modeling approach: qualitative deviations equations as a high levelmo deldescription language, and Constraint Satisfaction Problems (CSPs) with non binary constraints as underlying implementation formalism.
An implementation of qualitative equations systems based on non binary constraints is presented, discussing the applicability of various heuristics. In particular, a greedy heuristic algorithm for cycle cutset decomposition and variable ordering is proposed for efficient reasoning on CSPs derived from qualitative equations.
A prototype implementation of a constraint-based diagnostic engine has been developed using CLP(FD) and C++, and some preliminary results on the proposed modeling approach and heuristics are reported.
Partially supported by the European Commission, DG XII (project BE 95/2128).
VMBD (Vehicle Model-Based Diagnosis) is a Brite-Euram project concerning the application of model-based diagnostic techniques in automotive domains.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A. Bockmayr and T. Kasper. Branch-and-infer: A unifying framework for integer and finite domain constraint programming. INFORMS J. Computing, 10(3):287–300, 1998.
F. Cascio, L. Console, M. Guagliumi, M. Osella, A. Panati, S. Sottano, and D. Theseider Dupré. Generating on-board diagnostics of dynamic automotive systems based on qualitative models. AI Communications, 12:33–43, June 1999.
A. Davenport and E. Tsang. An empirical investigation into the exceptionally hard problems. Proc. Workshop on Constraint-based Reasoning, pages 46–53, 1995.
J. de Kleer. A comparison of ATMS and CSP techniques. Int. Joint Conference on Artificial Intelligence, 1989.
R. Dechter. Enhancement schemes for constraint processing: Backjumping, learning, and cutset decomposition. Artificial Intelligence, 41:273–312, 1990.
V. Kumar. Algorithms for constraint satisfaction problems: A survey. AI Magazine, 13(1):32–44, 1992.
A. Malik and P. Struss. Diagnosis of dynamic systems does not necessarily require simulation. Proc. 7th Int. Workshop on Principles ofDiagnosis, 1996.
D. Theseider Dupré and A. Panati. State-based vs simulation-based diagnosis of dynamic systems. In Proc. 9th International Workshop on Principles ofDiagnosis, pages 40–46, Cape Cod (USA), May 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Panati, A. (2000). Non Binary CSPs and Heuristics for Modeling and Diagnosing Dynamic Systems. In: Lamma, E., Mello, P. (eds) AI*IA 99: Advances in Artificial Intelligence. AI*IA 1999. Lecture Notes in Computer Science(), vol 1792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46238-4_15
Download citation
DOI: https://doi.org/10.1007/3-540-46238-4_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67350-7
Online ISBN: 978-3-540-46238-5
eBook Packages: Springer Book Archive