8 research outputs found
On Expert Behaviors and Question Types for Efficient Query-Based Ontology Fault Localization
We challenge existing query-based ontology fault localization methods wrt.
assumptions they make, criteria they optimize, and interaction means they use.
We find that their efficiency depends largely on the behavior of the
interacting expert, that performed calculations can be inefficient or
imprecise, and that used optimization criteria are often not fully realistic.
As a remedy, we suggest a novel (and simpler) interaction approach which
overcomes all identified problems and, in comprehensive experiments on faulty
real-world ontologies, enables a successful fault localization while requiring
fewer expert interactions in 66 % of the cases, and always at least 80 % less
expert waiting time, compared to existing methods
Do We Really Sample Right In Model-Based Diagnosis?
Statistical samples, in order to be representative, have to be drawn from a
population in a random and unbiased way. Nevertheless, it is common practice in
the field of model-based diagnosis to make estimations from (biased) best-first
samples. One example is the computation of a few most probable possible fault
explanations for a defective system and the use of these to assess which aspect
of the system, if measured, would bring the highest information gain.
In this work, we scrutinize whether these statistically not well-founded
conventions, that both diagnosis researchers and practitioners have adhered to
for decades, are indeed reasonable. To this end, we empirically analyze various
sampling methods that generate fault explanations. We study the
representativeness of the produced samples in terms of their estimations about
fault explanations and how well they guide diagnostic decisions, and we
investigate the impact of sample size, the optimal trade-off between sampling
efficiency and effectivity, and how approximate sampling techniques compare to
exact ones
Sound, Complete, Linear-Space, Best-First Diagnosis Search
Various model-based diagnosis scenarios require the computation of the most
preferred fault explanations. Existing algorithms that are sound (i.e., output
only actual fault explanations) and complete (i.e., can return all
explanations), however, require exponential space to achieve this task. As a
remedy, to enable successful diagnosis on memory-restricted devices and for
memory-intensive problem cases, we propose RBF-HS, a diagnostic search method
based on Korf's well-known RBFS algorithm. RBF-HS can enumerate an arbitrary
fixed number of fault explanations in best-first order within linear space
bounds, without sacrificing the desirable soundness or completeness properties.
Evaluations using real-world diagnosis cases show that RBF-HS, when used to
compute minimum-cardinality fault explanations, in most cases saves substantial
space (up to 98 %) while requiring only reasonably more or even less time than
Reiter's HS-Tree, a commonly used and as generally applicable sound, complete
and best-first diagnosis search
Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging
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.Comment: Habilitation Thesi