Auflistung nach Autor:in "Beierle, Christoph"
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- ZeitschriftenartikelA Practical Comparison of Qualitative Inferences with Preferred Ranking Models(KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Beierle, Christoph; Eichhorn, Christian; Kutsch, StevenWhen reasoning qualitatively from a conditional knowledge base, two established approaches are system Z and p-entailment. The latter infers skeptically over all ranking models of the knowledge base, while system Z uses the unique pareto-minimal ranking model for the inference relations. Between these two extremes of using all or just one ranking model, the approach of c-representations generates a subset of all ranking models with certain constraints. Recent work shows that skeptical inference over all c-representations of a knowledge base includes and extends p-entailment. In this paper, we follow the idea of using preferred models of the knowledge base instead of the set of all models as a base for the inference relation. We employ different minimality constraints for c-representations and demonstrate inference relations from sets of preferred c-representations with respect to these constraints. We present a practical tool for automatic c-inference that is based on a high-level, declarative constraint-logic programming approach. Using our implementation, we illustrate that different minimality constraints lead to inference relations that differ mutually as well as from system Z and p-entailment.
- 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.
- KonferenzbeitragAutomatic analysis of programming assignments(DeLFI 2003, Tagungsband der 1. e-Learning Fachtagung Informatik, 16.-18. September 2003 in Garching bei München., 2003) Beierle, Christoph; Kula, Marjaa; Widera, ManfredIn a virtual university, advanced support for all aspects of handling assignments is needed. Homework assignments are particularly in need of help because communication between teachers and learners as well as between learners is not as easy as in presence universities. In this paper, we present an overview of the $AT(x)$ approach (analyze-and-test) for automatically analyzing and testing programs. We describe how $AT(x)$ is used for giving feedback to students working on programming exercises. The $AT(x)$ framework is instantiated to $AT(P)$ and $AT(S)$ aiming at programs written in Prolog and Scheme, respectively.
- KonferenzbeitragEin erweiterbares interaktives Online-Übungssystem mit Aufgaben zu Aussagen- und Prädikatenlogik(DeLFI 2006, 4. e-Learning Fachtagung Informatik, 11. - 14. September 2006, in Darmstadt, Germany, 2006) Schulz-Gerlach, Immo; Beierle, ChristophUm insbesondere Fernstudierenden die Kontrolle des eigenen Lernerfolgs zu erleichtern und darüber hinaus durch aktive Hilfestellung den Lernerfolg der Studierenden noch steigern zu können, wurde ein vollautomatisch arbeitendes Online- Übungssystem realisiert, über welches derzeit interaktive Aufgaben zu Aussagenund Prädikatenlogik angeboten werden. In dieser Arbeit stellen wir das System, die derzeit implementierten Aufgaben und das derzeitige Einsatzgebiet an der FernUniversität in Hagen vor.
- 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
- JournalIntentional Forgetting Must be Part of the Functionality(KI - Künstliche Intelligenz: Vol. 33, No. 1, 2019) Timm, Ingo J.; Herzog, Otthein; Berndt, Jan Ole; Beierle, Christoph
- JournalIntentional Forgetting: A Huge Potential for Organizations(KI - Künstliche Intelligenz: Vol. 33, No. 1, 2019) Beierle, Christoph; Berndt, Jan Ole; Gronau, Norbert; Timm, Ingo J.
- JournalIntentional Forgetting: An Emerging Field in AI and Beyond(KI - Künstliche Intelligenz: Vol. 33, No. 1, 2019) Beierle, Christoph; Timm, Ingo J.
- 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).