Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
PR-OWL: A Framework for Probabilistic Ontologies
Proceedings of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006)
A Lightweight Approach to Distributed Network Diagnosis under Uncertainty
INCOS '09 Proceedings of the 2009 International Conference on Intelligent Networking and Collaborative Systems
Business Data Communications and Networking
Business Data Communications and Networking
Probabilistic fault diagnosis in the MAGNETO autonomic control loop
AIMS'10 Proceedings of the Mechanisms for autonomous management of networks and services, and 4th international conference on Autonomous infrastructure, management and security
Supporting rule system interoperability on the semantic web with SWRL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
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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.