Auflistung nach Autor:in "Kern-Isberner, Gabriele"
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- JournalAdvanced Solving Technology for Dynamic and Reactive Applications(KI - Künstliche Intelligenz: Vol. 32, No. 2-3, 2018) Brewka, Gerhard; Ellmauthaler, Stefan; Kern-Isberner, Gabriele; Obermeier, Philipp; Ostrowski, Max; Romero, Javier; Schaub, Torsten; Schieweck, Steffen
- ZeitschriftenartikelAspects of Universitarian Distance Teaching and Online Learning(KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Beierle, Christoph; Kern-Isberner, GabrieleAlready well before the arrival of the Internet and the world wide web, distance teaching universities were established in various countries. Using the ubiquitous availability of the WWW and advances in modern information and communication technologies, new and exciting online learning offerings like Udacity are now available. From a personal perspective, we discuss aspects of universitarian distance teaching and online learning and present thoughts about benefits the different learning forms may provide.
- JournalA Brief Survey on Forgetting from a Knowledge Representation and Reasoning Perspective(KI - Künstliche Intelligenz: Vol. 33, No. 1, 2019) Eiter, Thomas; Kern-Isberner, Gabriele
- ZeitschriftenartikelExtending and Completing Probabilistic Knowledge and Beliefs Without Bias(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Beierle, Christoph; Kern-Isberner, Gabriele; Finthammer, Marc; Potyka, NicoCombining logic with probability theory provides a solid ground for the representation of and the reasoning with uncertain knowledge. Given a set of probabilistic conditionals like “If A then B with probability x”, a crucial question is how to extend this explicit knowledge, thereby avoiding any unnecessary bias. The connection between such probabilistic reasoning and commonsense reasoning has been elaborated especially by Jeff Paris, advocating the principle of Maximum Entropy (MaxEnt). In this paper, we address the general concepts and ideas underlying MaxEnt and leading to it, illustrate the use of MaxEnt by reporting on an example application from the medical domain, and give a brief survey on recent approaches to extending the MaxEnt principle to first-order logic.
- WorkshopbeitragGenerierung interaktiver Selbsttestaufgaben im Bereich der formalen Grundlagen der Informatik aus XML-Spezifikationen(DeLFI 2005: 3. Deutsche e-Learning Fachtagung Informatik, 13. - 16. September 2005 in Rostock, Germany, 2005) Beierle, Christoph; Isberner, Malte; Kern-Isberner, Gabriele; Messing, Barbara; Widera, Manfred
- ZeitschriftenartikelMany Facets of Reasoning Under Uncertainty, Inconsistency, Vagueness, and Preferences: A Brief Survey(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Kern-Isberner, Gabriele; Lukasiewicz, ThomasIn this paper, we give an introduction to reasoning under uncertainty, inconsistency, vagueness, and preferences in artificial intelligence (AI), including some historic notes and a brief survey to previous approaches.
- KonferenzbeitragOn the computation of ranking functions for default rules – a challenge for constraint programming(INFORMATIK 2011 – Informatik schafft Communities, 2011) Beierle, Christoph; Kern-Isberner, GabrieleQualitative conditionals of the form If A then normally B can be viewed as default rules, and they require a semantical treatment going beyond the models used in classical logic. Ranking functions assigning degrees of plausibility to each possible world have been proposed as an appropriate semantic formalism. In this paper, we discuss the computation of c-representations corresponding to particular ranking functions for a set R of qualitative conditionals. As a challenge for constraint programming, we formulate a constraint satisfaction problem CR(R) as a declarative specification of all c-representations for R, and we argue that employing constraint programming techniques will be advantageous for computing all minimal solutions of CR(R).
- ZeitschriftenartikelQualitative and Semi-Quantitative Inductive Reasoning with Conditionals(KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Eichhorn, Christian; Kern-Isberner, GabrieleConditionals like “birds fly—if bird then fly” are crucial for commonsense reasoning. In this technical project report we show that conditional logics provide a powerful formal framework that helps understanding if-then sentences in a way that is much closer to human reasoning than classical logic and allows for high-quality reasoning methods. We describe methods that inductively generate models from conditional knowledge bases. For this, we use both qualitative (like preferential models) and semi-quantitative (like Spohn’s ranking functions) semantics. We show similarities and differences between the resulting inference relations with respect to formal properties. We further report on two graphical methods on top of the ranking approaches which allow to decompose the models into smaller, more feasible components and allow for local inferences.
- ZeitschriftenartikelSpecial Issue on Challenges for Reasoning under Uncertainty, Inconsistency, Vagueness, and Preferences(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Kern-Isberner, Gabriele; Lukasiewicz, Thomas
- KonferenzbeitragStepwise optimization of a constraint logic program for the computation of ranking functions(INFORMATIK 2012, 2012) Beierle, Christoph; Kern-Isberner, Gabriele; Södler, KarlOrdinal conditional functions (OCFs) can be used for assigning a semantics to qualitative conditionals of the form if A then (normally) B. The set of OCFs accepting all conditionals in a knowledge base R can be specified as the solutions of a constraint satisfaction problem CR(R). In this paper, we present three optimizations of a high-level, declarative CLP program solving CR(R) and illustrate the benefits of these optimizations by various examples.