# Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging

arXiv: https://arxiv.org/abs/2306.12850 · HTML: https://mlsanity.com/paper/2306.12850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 73 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 15 of 15)

- [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)
- [How should I compute my candidates? A taxonomy and classification of diagnosis computation algorithms](https://mlsanity.com/paper/2207.12583/) (2022)
- [Towards Better Response Times and Higher-Quality Queries in Interactive Knowledge Base Debugging](https://mlsanity.com/paper/1609.02584/) (2016)
- [A Theory of Interactive Debugging of Knowledge Bases in Monotonic Logics](https://mlsanity.com/paper/1609.06375/) (2016)
- [A Generally Applicable, Highly Scalable Measurement Computation and Optimization Approach to Sequential Model-Based Diagnosis](https://mlsanity.com/paper/1711.05508/) (2017)
- [Evaluating Active Learning Heuristics for Sequential Diagnosis](https://mlsanity.com/paper/1807.03083/) (2018)
- [A New Expert Questioning Approach to More Efficient Fault Localization in Ontologies](https://mlsanity.com/paper/1904.00317/) (2019)
- [Are Query-Based Ontology Debuggers Really Helping Knowledge Engineers?](https://mlsanity.com/paper/1904.01484/) (2019)
- [Towards Optimizing Reiter's HS-Tree for Sequential Diagnosis](https://mlsanity.com/paper/1907.12130/) (2019)
- [Understanding the QuickXPlain Algorithm: Simple Explanation and Formal Proof](https://mlsanity.com/paper/2001.01835/) (2020)
- [On Expert Behaviors and Question Types for Efficient Query-Based Ontology Fault Localization](https://mlsanity.com/paper/2001.05952/) (2020)
- [The Scheduling Job-Set Optimization Problem: A Model-Based Diagnosis Approach](https://mlsanity.com/paper/2009.11142/) (2020)
- [Do We Really Sample Right In Model-Based Diagnosis?](https://mlsanity.com/paper/2009.12178/) (2020)
- [Sound, Complete, Linear-Space, Best-First Diagnosis Search](https://mlsanity.com/paper/2009.12190/) (2020)

## Abstract (arXiv)

In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in e-commerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, the power grid to ensure our energy supply, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play. Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above, and many more. It exploits and orchestrates i.a. techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, optimization, stochastics, statistics, decision making under uncertainty, machine learning, as well as calculus, combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems. In this thesis, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these issues.