Network fault diagnosis using a realistic abductive reasoning model

Abstract

This paper proposes a method to solve the network fault diagnosis problem using the Realistic Abductive Reasoning Model. This model uses an abductive inference mechanism based on the parsimonious covering theory, and adds some new features to the general model of diagnostic problem-solving. The network fault-diagnosis knowledge is assumed to be represented in the form of causal chaining, namely, a hyper-bipartite graph. A layered graph is constructed from the given hyper-bipartite graph by the addition of a few dummy nodes. Then the diagnostic problem is solved, starting from the lowest layer of the layered graph, as a series of bipartite graphs, until the top-most layer is reached. The inference mechanism uses a Realistic Abductive Reasoning Model to diagnose the faults in a communication network, which is symptom-driven, based on some application programs. The hypothesis-test paradigm is used to refine the solution space. The fault-diagnostic capability of the proposed inference model is demonstrated by considering one node of a given network where the management information would be used to diagnose its local problems and the connectivity of the node in the network. The results obtained by the proposed model substantiate its effectiveness in solving network fault-diagnostic problems.