Network Fault Diagnosis Using Realistic Abductive Reasoning Model

Fault Diagnosis of Telecommunication Networks : Realistic Abductive Reasoning Approach

In this paper, we propose a method to diagnose the faults in the telecommunication networks by using the Realistic Abductive Reasoning Model 1]. This model uses abductive inference mechanism based on the parsimonious covering theory with some new features added to the general model of diagnostic problem solving. The fault diagnosis knowledge is assumed to be represented in the form of causal ch...

Network performance management using realistic abductive reasoning model

Performance degradation in communication networks can be viewed to be caused by a set of faults, called soft failures, owing to which the network resources like bandwidth can not be utilized to the expected level. An automated solution to the performance management problem involves identifying these soft failures and use/suggest suitable remedies to tune the network for better performance. Abdu...

Completing Fault Models for Abductive Diagnosis

Probabilistic Extention to Realistic Abductive Reasoning Model

In this paper, we give method for probabilistic assignment to the Realistic Abduc-tive Reasoning Model, proposed in 1]. The knowledge is assumed to be represented in the form of causal chaining, namely, hyper-bipartite network. Hyper-bipartite network is the most generalized form of knowledge representation for which, so far, there has been no way of assigning the probability to the explanation...

Model-Based Diagnosis or Reasoning from First Principles

model that lets us automate the task of locating a detected misbehavior’s cause.1 Although several ways to locate causes exist, they all involve searching for the parts of the system that might have caused the misbehavior. In recent years model-based diagnosis has proven successful in numerous application domains, and the underlying methods are now powerful enough to handle many real-world prob...