How should I compute my candidates? A taxonomy and classification of diagnosis computation algorithms

TL;DR

This paper addresses heterogeneity in diagnosis computation methods by introducing a formal taxonomy to characterize algorithms solving diagnostic $DPI$ problems. It defines the DPI as $DPI=\langle sd, comps, obs, meas \rangle$ and uses properties such as Soundness, Completeness, Best-First, Type of Output, Conflict Dependency, Way of Conflict Computation, Sequential Diagnosis, State Maintenance, General Applicability, Black-Box Reasoning, Logics-Agnosticism, and Space Efficiency to classify methods. It demonstrates usefulness by classifying prominent algorithms from 1987–2022 in a comprehensive tabular classification and discusses implications for fair evaluations and reuse. The work aims to guide researchers and practitioners in choosing appropriately efficient, sound, and complete approaches for a given $DPI$ and sequential settings, and to spur explicit reporting of properties to enable clearer context and comparisons.

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.