4 research outputs found
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
The Scheduling Job-Set Optimization Problem: A Model-Based Diagnosis Approach
A common issue for companies is that the volume of product orders may at
times exceed the production capacity. We formally introduce two novel problems
dealing with the question which orders to discard or postpone in order to meet
certain (timeliness) goals, and try to approach them by means of model-based
diagnosis. In thorough analyses, we identify many similarities of the
introduced problems to diagnosis problems, but also reveal crucial
idiosyncracies and outline ways to handle or leverage them. Finally, a
proof-of-concept evaluation on industrial-scale problem instances from a
well-known scheduling benchmark suite demonstrates that one of the two
formalized problems can be well attacked by out-of-the-box model-based
diagnosis tools
Visualising the effects of ontology changes and studying their understanding with ChImp
Due to the Semantic Web's decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes. We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies
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