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

19 pages•Published: January 6, 2018

Patrick Rodler, Wolfgang Schmid and Konstantin Schekotihin

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:	https://easychair.org/publications/paper/HhPf
	https://doi.org/10.29007/vd18

BibTeX entry

@inproceedings{DX'17:Inexpensive_Cost_Optimized_Measurement,
  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},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/HhPf},
  doi       = {10.29007/vd18},
  pages     = {200-218},
  year      = {2018}}

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