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Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis

19 pagesPublished: January 6, 2018

Abstract

In this work we present strategies for (optimal) measurement computation and selection in model- based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and optimized along two dimensions: expected number of queries and cost per query. By means of a suitable decoupling of two optimizations and a clever search space reduction the computations are done without any inference engine calls. For the full search space, we give a method requiring only a polynomial number of inferences and guarantee- ing query properties existing methods do not provide. Evaluation results using real-world problems indicate that the new method computes (virtually) optimal queries instantly independently of the size and complexity of the considered diagnosis problems.

Keyphrases: measurement selection, query generation, sequential diagnosis

In: Marina Zanella, Ingo Pill and Alessandro Cimatti (editors). 28th International Workshop on Principles of Diagnosis (DX'17), vol 4, pages 200--218

Links:
BibTeX entry
@inproceedings{DX'17:Inexpensive_Cost_Optimized_Measurement_Proposal,
  author    = {Patrick Rodler and Wolfgang Schmid and Konstantin Schekotihin},
  title     = {Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis},
  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     = {200--218},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, http://www.easychair.org},
  issn      = {2515-1762},
  url       = {https://easychair.org/publications/paper/HhPf},
  doi       = {10.29007/vd18}}
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