,
Johan de Kleer
Creative Commons Attribution 4.0 International license
Diagnoses address a universal need for explanations that arises whenever things go wrong or when we observe some unexpected behavior. The ubiquitous need for such explanations has led to an abundance of algorithmic concepts and computation variants for diagnostic processes. Comparisons have mainly focused on highlighting the virtues of individual algorithms or the required computational efforts. In this paper, we focus on evaluating the results of a diagnosis algorithm in the context of an inspection and repair process. We completely ignore the algorithm’s computation concept and whether the observations would justifiably support multiple diagnoses. In particular, we focus on inspecting and discussing the practical utility of diagnoses as a measure of the unnecessary efforts one has to spend when fully repairing a system. We start with assessing the utility of a single diagnosis and then progress to evaluating ambiguity groups, i.e., sets of diagnoses. We propose corresponding metrics that investigate worst case and average performance, contrast them to the utility metric used in the DX Competition 2010, and discuss the metrics' results for several scenarios.
@InProceedings{pill_et_al:OASIcs.DX.2026.9,
author = {Pill, Ingo and de Kleer, Johan},
title = {{On the Practical Utility of Diagnoses}},
booktitle = {37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
pages = {9:1--9:20},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-455-0},
ISSN = {2190-6807},
year = {2026},
volume = {148},
editor = {Pill, Ingo and Zanella, Marina and Provan, Gregory},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.9},
URN = {urn:nbn:de:0030-drops-278233},
doi = {10.4230/OASIcs.DX.2026.9},
annote = {Keywords: Model-based Diagnosis, Diagnosis, Algorithms}
}