Today’s complex production systems allow to si- multaneously build different products following individual production plans. Such plans may fail due to component faults or unforeseen behavior, resulting in flawed products. In this paper, we propose a method to integrate diagnosis with plan assessment to prevent plan failure, and to gain di- agnostic information when needed. In our setting, plans are generated from a planner before being executed on the system. If the underlying sys- tem drifts due to component faults or unforeseen behavior, plans that are ready for execution or already being executed are uncertain to succeed or fail. Therefore, our approach tracks plan exe- cution using probabilistic hierarchical constraint automata (PHCA) models of the system. This allows to explain past system behavior, such as observed discrepancies, while at the same time it can be used to predict a plan’s remaining chance of success or failure. We propose a formulation of this combined diagnosis/assessment problem as a constraint optimization problem, and present a fast solution algorithm that estimates success or failure probabilities by considering only a limited number k of system trajectories.
How to Cite
diagnosis, model based diagnostics, optimization, self-diagnosis
(Beetz, 2000) Michael Beetz. Concurrent Reactive Plans: Anticipating and Forestalling Execution Failures, volume 1772 of Lecture Notes in Artificial Intelligence. Springer Publishers, 2000.
(Bouveret et al., 2004) S. Bouveret, F. Heras, S.de Givry, J. Larrosa, M. Sanchez, and T. Schiex. Toolbar: a state-of-the-art platform for wcsp. www.inra.fr/mia/T/degivry/ToolBar.pdf, 2004.
(Kask and Dechter, 1999) Kalev Kask and Rina Dechter. Mini-bucket heuristics for improved search. In Proc. UAI-1999, pages 314–32, 1999.
(Krause and Guestrin, 2007) Andreas Krause and Carlos Guestrin. Near-optimal observation se- lection using submodular functions. In Proc. AAAI-2007, pages 1650–1654. AAAI Press, 2007.
(Kuhn et al., 2008) Lukas Kuhn, Bob Price, Johan de Kleer, Minh Binh Do, and Rong Zhou. Pervasive diagnosis: The integration of diagnostic goals into production plans. In Proc. AAAI-2008, 2008.
(Kurien and Nayak, 2000) James Kurien and P. Pan- durang Nayak. Back to the future for consistency- based trajectory tracking. In Proc. AAAI-2000, pages 370–377, 2000.
(Mahtab et al., 2004) Tazeen Mahtab, Greg Sullivan, and Brian C. Williams. Automated Verification of Model-based Programs Under Uncertainty. In Pro- ceedings 4th International Conference on Intelli- gent Systems Design and Application, 2004.
(McDermott, 1993) Drew McDermott. A reactive plan language. Technical report, Yale University, Computer Science Dept., 1993.
(Mikaelian et al., 2005) Tsoline Mikaelian, Brian C. Williams, and Martin Sachenbacher. Model- based Monitoring and Diagnosis of Systems with Software-Extended Behavior. In Proc. AAAI-05, 2005.
(Schiex et al., 1995) Thomas Schiex, He ́le`ne Fargier, and Gerard Verfaillie. Valued constraint satisfaction problems: hard and easy problems. In Proc. IJCAI- 1995, 1995.
(Williams et al., 2001) Brian C. Williams, Seung Chung, and Vineet Gupta.Mode estimation of model-based programs monitoring system with complex behavior.In Proc. IJCAI-2001, pages 579–590, 2001.
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.