Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis
Abstract
In this work we present strategies for (optimal) measurement 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 guaranteeing query properties existing methods cannot 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.
Cite
@article{arxiv.1705.09879,
title = {Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis},
author = {Patrick Rodler and Wolfgang Schmid and Konstantin Schekotihin},
journal= {arXiv preprint arXiv:1705.09879},
year = {2017}
}