How should I compute my candidates? A taxonomy and classification of diagnosis computation algorithms
Abstract
This work proposes a taxonomy for diagnosis computation methods which allows their standardized assessment, classification and comparison. The aim is to (i) give researchers and practitioners an impression of the diverse landscape of available diagnostic techniques, (ii) allow them to easily retrieve the main features as well as pros and cons of the approaches, (iii) enable an easy and clear comparison of the techniques based on their characteristics wrt. a list of important and well-defined properties, and (iv) facilitate the selection of the "right" algorithm to adopt for a particular problem case, e.g., in practical diagnostic settings, for comparison in experimental evaluations, or for reuse, modification, extension, or improvement in the course of research.
Keywords
Cite
@article{arxiv.2207.12583,
title = {How should I compute my candidates? A taxonomy and classification of diagnosis computation algorithms},
author = {Patrick Rodler},
journal= {arXiv preprint arXiv:2207.12583},
year = {2024}
}
Comments
Updated version including a reference to the published peer-reviewed version of this work, which appeared in the proceedings of the European Conference on Artificial Intelligence (ECAI) 2023