Download PDFOpen PDF in browser

Reducing Model-Based Diagnosis to Knowledge Base Debugging

13 pagesPublished: January 6, 2018

Abstract

Model-Based Diagnosis (MBD) is a principled approach to fault localization in any type of system that can be described in a formal structured way. Knowledge Base Debugging (KBD) draws on concepts from MBD to find faults in a monotonic knowledge base. We show that KBD is a generalization of MBD in that any MBD problem can be reduced to a KBD problem and solutions of the former can be directly extracted from solutions of the latter. Moreover, we find that the sequential MBD problem is a special case of the sequential KBD problem in that the latter allows a user to provide more types of measurements. As a consequence of these results, KBD approaches can be applied to all systems amenable to MBD.

Keyphrases: Knowledge base debugging, model-based diagnosis, Problem reduction

In: Marina Zanella, Ingo Pill and Alessandro Cimatti (editors). 28th International Workshop on Principles of Diagnosis (DX'17), vol 4, pages 284--296

Links:
BibTeX entry
@inproceedings{DX'17:Reducing_Model_Based_Diagnosis_to,
  author    = {Patrick Rodler and Konstantin Schekotihin},
  title     = {Reducing Model-Based Diagnosis to Knowledge Base Debugging},
  booktitle = {28th International Workshop on Principles of Diagnosis (DX'17)},
  editor    = {Marina Zanella and Ingo Pill and Alessandro Cimatti},
  series    = {Kalpa Publications in Computing},
  volume    = {4},
  pages     = {284--296},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, http://www.easychair.org},
  issn      = {2515-1762},
  url       = {https://easychair.org/publications/paper/3g9Q},
  doi       = {10.29007/p7zp}}
Download PDFOpen PDF in browser