Diagnosis of Constant Faults in Switching Networks

TL;DR

This paper investigates the use of decision trees to diagnose constant faults in switching networks, focusing on the depth and complexity of constructing such trees for different network types.

Contribution

It introduces a decision tree-based approach for diagnosing constant faults in switching networks and analyzes their depth and construction complexity.

Findings

Decision trees can effectively diagnose constant faults.

The depth of decision trees varies with network type.

Complexity results for constructing diagnostic decision trees.

Abstract

In this paper, we study decision trees for diagnosis of constant faults in switching networks. Each constant fault consists in assigning Boolean constants to some edges of the network instead of literals. The problem of diagnosis is to recognize the function implemented by the switching network with a constant fault from a given set of faults. For this problem solving, we use decision trees. Each query (attribute) of a decision tree consists of observing the value of function implemented by the faulty switching network on a given tuple of variable values. We study the depth of decision trees for diagnosis of arbitrary and specially constructed switching networks and the complexity of diagnostic decision tree construction.