565 research outputs found
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
Query Answer Explanations under Existential Rules
Ontology-mediated query answering is an extensively studied paradigm, which aims at improving
query answers with the use of a logical theory. In this paper, we focus on ontology languages based on
existential rules, and we carry out a thorough complexity analysis of the problem of explaining query
answers in terms of minimal subsets of database facts and related task
Conservation GIS: Ontology and spatial reasoning for commonsense knowledge.
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.Geographic information available from multiple sources are moving beyond their local
context and widening the semantic difference. The major challenge emerged with ubiquity of
geographic information, evolving geospatial technology and location-aware service is to deal
with the semantic interoperability. Although the use of ontology aims at capturing shared
conceptualization of geospatial information, human perception of world view is not
adequately addressed in geospatial ontology. This study proposes âConservation GIS
Ontologyâ that comprises spatial knowledge of non-expert conservationists in the context of
Chitwan National Park, Nepal.
The discussion is presented in four parts: exploration of commonsense spatial knowledge
about conservation; development of conceptual ontology to conceptualize domain
knowledge; formal representation of conceptualization in Web Ontology Language (OWL);
and quality assessment of the ontology development tasks. Elicitation of commonsense
spatial knowledge is performed with the notion of cognitive view of semantic. Emphasis is
given to investigate the observation of wildlife movement and habitat change scenarios.
Conceptualization is carried out by providing the foundation of the top-level ontology-
âDOLCEâ and geospatial ontologies. ProtĂ©gĂ© 4.1 ontology editor is employed for ontology
engineering tasks. Quality assessment is accomplished based on the intrinsic approach of
ontology evaluation.(...
Cognition Matters: Enduring Questions in Cognitive IS Research
We explore the history of cognitive research in information systems (IS) across three major research streams in which cognitive processes are of paramount importance: developing software, decision support, and human-computer interaction. Through our historical analysis, we identify âenduring questionsâ in each area. The enduring questions motivated long-standing areas of inquiry within a particular research stream. These questions, while perhaps unapparent to the authors cited, become evident when one adopts an historical perspective. While research in all three areas was influenced by changes in technologies, research techniques, and the contexts of use, these enduring questions remain fundamental to our understanding of how to develop, reason with, and interact with IS. In synthesizing common themes across the three streams, we draw out four cognitive qualities of information technology: interactivity, fit, cooperativity, and affordances. Together these cognitive qualities reflect ITâs ability to influence cognitive processes and ultimately task performance. Extrapolating from our historical analysis and looking at the operation of these cognitive qualities in concert, we envisage a bright future for cognitive research in IS: a future in which the study of cognition in IS extends beyond the individual to consider cognition distributed across teams, communities and systems, and a future involving the study of rich and dynamic social and organizational contexts in which the interplay between cognition, emotion, and attitudes provides a deeper explanation of behavior with IS
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