Abstract
This article proposes a (MAS) architecture for network diagnosis under uncertainty. Network diagnosis is divided into two inference processes: hypotheses generation and hypotheses confirmation. The first process is distributed among several agents based on a (MSBN), while the second one is carried out by agents using semantic reasoning. A diagnosis ontology has been defined in order to combine both reasoning processes. To drive the deliberation process, the strength of influence obtained from (CDF) method is used during diagnosis process. In order to achieve quick and reliable diagnoses, this influence is used to choose the best action to perform. This approach has been evaluated in a P2P video streaming scenario. Computational and time improvements are highlighted as conclusions.
Keywords
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
Arozarena, P., Toribio, R., Kielthy, J., Quinn, K., Zach, M.: Probabilistic Fault Diagnosis in the MAGNETO Autonomic Control Loop. In: Stiller, B., De Turck, F. (eds.) AIMS 2010. LNCS, vol. 6155, pp. 102–105. Springer, Heidelberg (2010)
Benjamins, R.: Problem-solving methods for diagnosis and their role. International Journal of Expert Systems: Research and Applications 8(2), 93–120 (1995)
Berthet, G., Fischer, N.: A unified theory of fault diagnosis and distributed fault management in communication networks. In: Proceedings of IEEE AFRICON 1996, pp. 776–781. IEEE (1995)
Carrera, A., Iglesias, C.A.: B2DI - A Bayesian BDI Agent Model with Causal Belief Updating based on MSBN. In: Proceedings of the 4th International Conference on Agents and Artificial Intelligence, pp. 343–346 (2012)
Costa, P.C.G., Laskey, K.B.: PR-OWL: A framework for probabilistic ontologies. In: Proceedings of the 2006 Fourth International Conference on Formal Ontology in Information Systems, FOIS 2006, pp. 237–249. IOS Press (2006)
FitzGerald, J., Dennis, A.: Business Data Communications and Networking. John Wiley and Sons (2008)
García-Algarra, F.J., Arozarena-Llopis, P., García-Gómez, S., Carrera-Barroso, A.: A lightweight approach to distributed network diagnosis under uncertainty. In: INCOS 2009: Proceedings of the 2009 International Conference on Intelligent Networking and Collaborative Systems, pp. 74–80. IEEE Computer Society, Washington, DC (2009)
Kjaerulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams. Information Science and Statistics. Springer, New York (2008)
Kraaijeveld, P., Druzdzel, M., Onisko, A., Wasyluk, H.: Genierate: An interactive generator of diagnostic bayesian network models. In: Proc. 16th Int. Workshop Principles Diagnosis, pp. 175–180. Citeseer (2005)
O’Connor, M., Knublauch, H., Tu, S., Grosof, B., Dean, M., Grosso, W., Musen, M.: Supporting Rule System Interoperability on the Semantic Web with SWRL. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 974–986. Springer, Heidelberg (2005)
Sedano-Frade, A., González-Ordás, J., Arozarena-Llopis, P., García-Gómez, S., Carrera-Barroso, A.: Distributed Bayesian Diagnosis for Telecommunication Networks. In: Demazeau, Y., Dignum, F., Corchado, J.M., Pérez, J.B. (eds.) Advances in PAAMS. AISC, vol. 70, pp. 231–240. Springer, Heidelberg (2010)
Xiang, Y.: Belief updating in multiply sectioned Bayesian networks without repeated local propagations. International Journal of Approximate Reasoning 23(1), 1–21 (2000)
Xiang, Y., Poole, D., Beddoes, M.P.: Multiply Sectioned Bayesian Networks and Junction Forests for Large Knowledge-based Systems. Computational Intelligence 9(2), 171–220 (1993)
Wang, X.H., Zhang, D.Q.: Ontology based context modeling and reasoning using OWL. IEEE (March 2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carrera, Á., Iglesias, C.A. (2012). Improving Diagnosis Agents with Hybrid Hypotheses Confirmation Reasoning Techniques. In: Cossentino, M., Kaisers, M., Tuyls, K., Weiss, G. (eds) Multi-Agent Systems. EUMAS 2011. Lecture Notes in Computer Science(), vol 7541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34799-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-34799-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34798-6
Online ISBN: 978-3-642-34799-3
eBook Packages: Computer ScienceComputer Science (R0)