This article proposes a MAS architecture for network diagnosis under uncertainty. Network diagnosis is divided into two inference processes: hypothesis generation and hypothesis conﬁrmation. The ﬁrst process is distributed among several agents based on a Multiply Sectioned Bayesian Network (MSBN), while the second one is carried out by agents using semantic reasoning. A diagnosis ontology has been deﬁned in order to combine both inference processes.
To drive the deliberation process, dynamic data about the inﬂuence of observable variables (data) are taken 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 highlight as conclusions.