Improving Diagnosis Agents with Hybrid Hypotheses Confirmation Reasoning Techniques

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7541)


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.


agent Bayesian ontology diagnosis network 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Universidad Politécnica de MadridMadridSpain

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