Abstract
Previous works have proposed to apply model-based diagnosis (MBD) techniques to detect and locate faults in the control software of mobile autonomous robots at runtime. The localization of faults at the level of software components enables the autonomous repair of the system by restarting failed components. Unfortunately, classical MBD approaches assume that components fail independently. In this paper we show that dependent failures are very common in this application domain and we propose the concept of diagnosis environments (DEs) in order to tackle the arising problems. We provide an algorithm for the computation of DEs and present the results of case studies.
This research has been funded in part by the Austrian Science Fund (FWF) under grant P17963-N04. Authors are listed in alphabetical order.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Weber, J., Wotawa, F.: Using AI techniques for fault localization in component-oriented software systems. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, Springer, Heidelberg (2006)
Reiter, R.: A theory of diagnosis from first principles. Artificial Intelligence 32(1), 57–95 (1987)
de Kleer, J., Williams, B.C.: Diagnosing multiple faults. Artificial Intelligence 32(1), 97–130 (1987)
Steinbauer, G., Wotawa, F.: Detecting and locating faults in the control software of autonomous mobile robots. In: Proceedings of the 19th International Joint Conf. on Artificial Intelligence, Edinburgh, UK, pp. 1742–1743 (2005)
de Kleer, J.: Using crude probability estimates to guide diagnosis. Artificial Intelligence 45, 381–392 (1990)
Struss, P., Dressler, O.: Physical negation: Integrating fault models into the general diagnostic engine. In: Proceedings of the 11th International Joint Conf. on Artificial Intelligence, pp. 1318–1323 (1989)
Minoux, M.: LTUR: A Simplified Linear-time Unit Resolution Algorithm for Horn Formulae and Computer Implementation. Information Processing Letters 29, 1–12 (1988)
Lucas, P.J.F.: Bayesian model-based diagnosis. International Journal of Approx. Reasoning 27(2), 99–119 (2001)
Davis, R.: Diagnostic reasoning based on structure and behavior. Artificial Intelligence 24, 347–410 (1984)
Böttcher, C.: No faults in structure? how to diagnose hidden interactions. In: IJCAI, pp. 1728–1735 (1995)
de Kleer, J., Williams, B.C.: Diagnosis with behavioral modes. In: Proceedings of the 11th International Joint Conf. on Artificial Intelligence, pp. 1324–1330 (1989)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Weber, J., Wotawa, F. (2007). Diagnosing Dependent Failures in the Hardware and Software of Mobile Autonomous Robots . In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_63
Download citation
DOI: https://doi.org/10.1007/978-3-540-73325-6_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73322-5
Online ISBN: 978-3-540-73325-6
eBook Packages: Computer ScienceComputer Science (R0)