Model-based Probabilistic Diagnosis in Large Cyberphysical Systems
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
https://orcid.org/0000-0002-5391-3087
Giso Dal Arjen Hommersom
Guus Grievink
Abstract
Model-based diagnosis is concerned with diagnosing faults or malfunction of real-world physical or cyberphysical systems using a model of the structure and behavior of the systems. As cyberphysical systems can be extremely large and complex, and the associated computational models will be then equally large and complex, they impose a hard to beat challenge on the computational feasibility of reasoning with such models. When such a model is able to handle the uncertainty associated with diagnostics, giving rise to probabilistic model-based diagnostics, the computational feasibility becomes even harder. This paper: (1) proposes a novel graphical method underlying model-based diagnostics; (2) demonstrates experimentally how a novel, by the authors developed architecture of partitioned positive weighted model counting, is able to handle exact inference to answer a variety of probabilistic queries regarding the health status of a cyberphysical system. Results obtained are well within acceptable time bounds.
How to Cite
##plugins.themes.bootstrap3.article.details##
Bayesian model-based diagnosis, Bayesian networks, Weighted model counting, Probabilistic inference
Chavira, M., & Darwiche, A. (2008). On probabilistic inference by weighted model counting. Artificial Intelligence, 172, 772-799.
Dal, G. H., Laarman, A., & Lucas, P. J. F. (2023). ParaGnosis: A tool for parallel knowledge compilation. In Model checking software: 29th international symposium, SPIN 2023, paris, france, april 26-27, 2023, proceedings (pp. 22-37).
Dal, G. H., Laarman, A. W., Hommersom, A., & Lucas, P. J. F. (2021). A compositional approach to probabilistic knowledge compilation. International Journal of Approximate Reasoning, 138, 38-66.
Dal, G. H., & Lucas, P. J. F. (2017). Weighted positive binary decision diagrams for exact probabilistic inference. International Journal of Approximate Reasoning, 90, 411-432.
Dal, G. H., Michels, S., & Lucas, P. J. F. (2017). Reducing the cost of probabilistic knowledge compilation. In Proceedings of Machine Learning Research (Vol. 73, pp. 141-152).
Darwiche, A., & Marquis, P. (2002). A knowledge compilation map. Journal of Artificial Intelligence Research, 17, 229-264.
Darwiche, A., & Marquis, P. (2021). On quantifying literals in Boolean logic and its applications to explainable AI. Journal of Artificial Intelligence Research, 72, 285-328.
de Kleer, J. (1991). Focusing on probable diagnoses. In Proc. of AAAI-1991 (pp. 842-848).
de Kleer, J., Mackworth, A. K., & Reiter, R. (1992). Characterizing diagnoses and systems. Artificial Intelligence, 56(2-3), 197-222. doi: 10.1016/0004-3702(92)90027-u
de Kleer, J., & Williams, B. C. (1987). Diagnosing multiple faults. Artificial Intelligence, 32(1), 97-130. doi:10.1016/0004-3702(87)90063-4
Dowdeswell, B., Sinha, R., & MacDonell, S. G. (2020). Finding faults: A scoping study of fault diagnostics for Industrial Cyber-Physical Systems. Journal of Systems and Software, 168, 110638. doi:10.1016/j.jss.2020.110638
Dudek, J. M., Phan, V., & Vardi, M. Y. (2020). ADDMC: Weighted model counting with algebraic decision diagrams. In International Conference on Artificial Intelligence (pp. 1468-1476).
Genesereth, M., & Nilsson, N. (1987). Logical foundations of Artificial Intelligence. Morgan Kaufmann.
Grievink, G. (2022). Model-based probabilistic diagnostics of cyber-physical systems. Bachelor Thesis, University of Twente, The Netherlands.
Jensen, K., & Andreassen, S. (2008). Generic causal probabilistic networks: A solution to a problem of transferability in medical decision support. Computer Methods and Programs in Biomedicine, 89(2), 189-201. doi:10.1016/j.cmpb.2007.10.015
Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.
Lee, E. A. (2008). Cyber physical systems: Design challenges. In 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC) (pp. 363-369). doi:10.1109/ISORC.2008.25
Lucas, P. J. F. (1996). Structures in Diagnosis: from theoryto clinical application (Published PhD Thesis). Free University (VU) of Amsterdam, The Netherlands.
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan kaufmann.
R Core Team. (2024). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from https://www.R-project.org
Reiter, R. (1987). A theory of diagnosis from first principles. Artificial Intelligence, 32(1), 57-95. doi:10.1016/0004-3702(87)90062-2
Ruijters, E., & Stoelinga, M. (2015). Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review, 15-16, 29-62. doi:10.1016/j.cosrev.2015.03.001
Scutari, M. (2024). bnlearn - an R package for Bayesian network learning and inference [Computer software manual]. Retrieved from https://bnlearn.com
Srinivas, S. (1994). A probabilistic approach to hierarchical model-based diagnosis. In Uncertainty in Artifical Intelligence (pp. 538-545). Elsevier.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.