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

arXiv: https://arxiv.org/abs/2207.12583 · HTML: https://mlsanity.com/paper/2207.12583/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 60 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets](https://mlsanity.com/paper/2102.09005/) (2021)
- [The minimal hitting set generation problem: algorithms and computation](https://mlsanity.com/paper/1601.02939/) (2016)
- [Interactive Debugging of Knowledge Bases](https://mlsanity.com/paper/1605.05950/) (2016)
- [DynamicHS: Streamlining Reiter's Hitting-Set Tree for Sequential Diagnosis](https://mlsanity.com/paper/2012.11078/) (2020)
- [Sequential Diagnosis by Abstraction](https://mlsanity.com/paper/1401.3892/) (2014)

## Built on by (most cited first, 3 of 3)

- [Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging](https://mlsanity.com/paper/2306.12850/) (2023)
- [Diagnosis driven Anomaly Detection for CPS](https://mlsanity.com/paper/2311.15924/) (2023)
- [A Unified Framework for Runtime Verification and Model-Based Diagnosis in LOLA](https://mlsanity.com/paper/2606.23720/) (2026)

## Abstract (arXiv)

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.