7,667 research outputs found
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
Operational Research in Education
Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved in
most strategic decisions. However, few data-driven tools that support strategic
decision-making are available. We introduce and extend the use of autoencoders
to provide strategically relevant granular feedback. A first experiment
indicates that experts are inconsistent in their decision making, highlighting
the need for strategic decision support. Furthermore, using two large
industry-provided human resources datasets, the proposed solution is evaluated
in terms of ranking accuracy, synergy with human experts, and dimension-level
feedback. This three-point scheme is validated using (a) synthetic data, (b)
the perspective of data quality, (c) blind expert validation, and (d)
transparent expert evaluation. Our study confirms several principal weaknesses
of human decision-making and stresses the importance of synergy between a model
and humans. Moreover, unsupervised learning and in particular the autoencoder
are shown to be valuable tools for strategic decision-making
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