How Should I Compute My Candidates? A Taxonomy and Classification of Diagnosis Computation Algorithms

Abstract

Model-based diagnosis is a powerful, versatile and well-founded approach to troubleshooting a wealth of different types of systems. Diagnosis algorithms are both numerous and highly heterogeneous. In this work, we propose a taxonomy that allows their standardized assessment, classification and comparison. The aim is to (i) give researchers and practitioners an impression of the diverse landscape of available techniques, (ii) allow them to easily retrieve and compare the main features as well as pros and cons, and (iii) 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. Finally, we demonstrate the value and application of the taxonomy by assessing and categorizing a range of more than 30 important diagnostic methods, and we point out how using the taxonomy as a common guideline for algorithm analysis would benefit the research community in various regards.