27 found
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  1. Ranking Theory.Gabriele Kern-Isberner, Niels Skovgaard-Olsen & Wolfgang Spohn - forthcoming - Knauff, M. & Spohn, W. (Eds). The Handbook of Rationality. MIT Press.
    Ranking theory is one of the salient formal representations of doxastic states. It differs from others in being able to represent belief in a proposition (= taking it to be true), to also represent degrees of belief (i.e. beliefs as more or less firm), and thus to generally account for the dynamics of these beliefs. It does so on the basis of fundamental and compelling rationality postulates and is hence one way of explicating the rational structure of doxastic states. Thereby (...)
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  2.  26
    Structural Inference From Conditional Knowledge Bases.Gabriele Kern-Isberner & Christian Eichhorn - 2014 - Studia Logica 102 (4):751-769.
    There are several approaches implementing reasoning based on conditional knowledge bases, one of the most popular being System Z (Pearl, Proceedings of the 3rd conference on theoretical aspects of reasoning about knowledge, TARK ’90, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 121–135, 1990). We look at ranking functions (Spohn, The Laws of Belief: Ranking Theory and Its Philosophical Applications, Oxford University Press, Oxford, 2012) in general, conditional structures and c-representations (Kern-Isberner, Conditionals in Nonmonotonic Reasoning and Belief Revision: Considering (...)
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  3.  50
    Prioritized and Non-Prioritized Multiple Change on Belief Bases.Marcelo A. Falappa, Gabriele Kern-Isberner, Maurício D. L. Reis & Guillermo R. Simari - 2012 - Journal of Philosophical Logic 41 (1):77-113.
    In this article we explore multiple change operators, i.e., operators in which the epistemic input is a set of sentences instead of a single sentence. We propose two types of change: prioritized change, in which the input set is fully accepted, and symmetric change, where both the epistemic state and the epistemic input are equally treated. In both kinds of operators we propose a set of postulates and we present different constructions: kernel changes and partial meet changes.
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  4. Qualitative Probabilistic Inference with Default Inheritance.Paul D. Thorn, Christian Eichhorn, Gabriele Kern-Isberner & Gerhard Schurz - 2015 - In Christoph Beierle, Gabriele Kern-Isberner, Marco Ragni & Frieder Stolzenburg (eds.), Proceedings of the Ki 2015 Workshop on Formal and Cognitive Reasoning. pp. 16-28.
    There are numerous formal systems that allow inference of new conditionals based on a conditional knowledge base. Many of these systems have been analysed theoretically and some have been tested against human reasoning in psychological studies, but experiments evaluating the performance of such systems are rare. In this article, we extend the experiments in [19] in order to evaluate the inferential properties of c-representations in comparison to the well-known Systems P and Z. Since it is known that System Z and (...)
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  5.  42
    Stratified Belief Bases Revision with Argumentative Inference.Marcelo Alejandro Falappa, Alejandro Javier García, Gabriele Kern-Isberner & Guillermo Ricardo Simari - 2013 - Journal of Philosophical Logic 42 (1):161-193.
    We propose a revision operator on a stratified belief base, i.e., a belief base that stores beliefs in different strata corresponding to the value an agent assigns to these beliefs. Furthermore, the operator will be defined as to perform the revision in such a way that information is never lost upon revision but stored in a stratum or layer containing information perceived as having a lower value. In this manner, if the revision of one layer leads to the rejection of (...)
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  6.  9
    Formal Nonmonotonic Theories and Properties of Human Defeasible Reasoning.Marco Ragni, Christian Eichhorn, Tanja Bock, Gabriele Kern-Isberner & Alice Ping Ping Tse - 2017 - Minds and Machines 27 (1):79-117.
    The knowledge representation and reasoning of both humans and artificial systems often involves conditionals. A conditional connects a consequence which holds given a precondition. It can be easily recognized in natural languages with certain key words, like “if” in English. A vast amount of literature in both fields, both artificial intelligence and psychology, deals with the questions of how such conditionals can be best represented and how these conditionals can model human reasoning. On the other hand, findings in the psychology (...)
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  7.  7
    What Kind of Independence Do We Need for Multiple Iterated Belief Change.Gabriele Kern-Isberner & Daniela Huvermann - 2017 - Journal of Applied Logic 22:91-119.
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  8.  18
    Handling Conditionals Adequately in Uncertain Reasoning and Belief Revision.Gabriele Kern-Isberner - 2002 - Journal of Applied Non-Classical Logics 12 (2):215-237.
    Conditionals are most important objects in knowledge representation, commonsense reasoning and belief revision. Due to their non-classical nature, however, they are not easily dealt with. This paper presents a new approach to conditionals, which is apt to capture their dynamic power particularly well. We show how this approach can be applied to represent conditional knowledge inductively, and to guide revisions of epistemic states by sets of beliefs. In particular, we generalize system-Z* as an appropriate counterpart to maximum entropy-representations in a (...)
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  9.  8
    Dynamics of Knowledge and Belief.Christoph Beierle & Gabriele Kern-Isberner - 2016 - Journal of Applied Logic 19:51-53.
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  10.  5
    Editorial: Dynamics of Knowledge and Belief.Christoph Beierle & Gabriele Kern-Isberner - 2010 - Logic Journal of the IGPL 18 (4):485-487.
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  11.  5
    Properties and Interrelationships of Skeptical, Weakly Skeptical, and Credulous Inference Induced by Classes of Minimal Models.Christoph Beierle, Christian Eichhorn, Gabriele Kern-Isberner & Steven Kutsch - 2021 - Artificial Intelligence 297:103489.
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  12. Proceedings of the 4th Workshop on Dynamics of Knowledge and Belief (DKB-2013).Christoph Beierle & Gabriele Kern-Isberner (eds.) - 2013 - Fakultät für Mathematik und Informatik, FernUniversität in Hagen.
     
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  13. Proceedings of the KI 2015 Workshop on Formal and Cognitive Reasoning.Christoph Beierle, Gabriele Kern-Isberner, Marco Ragni & Frieder Stolzenburg (eds.) - 2015
     
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  14.  1
    Using Inductive Reasoning for Completing OCF-Networks.Christian Eichhorn & Gabriele Kern-Isberner - 2015 - Journal of Applied Logic 13 (4):605-627.
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  15. Explanations, Belief Revision and Defeasible Reasoning.Marcelo A. Falappa, Gabriele Kern-Isberner & Guillermo R. Simari - 2002 - Artificial Intelligence 141 (1-2):1-28.
  16.  39
    Conditional Indifference and Conditional Preservation.Gabriele Kern-Isberner - 2001 - Journal of Applied Non-Classical Logics 11 (1-2):85-106.
    The idea of preserving conditional beliefs emerged recently as a new paradigm apt to guide the revision of epistemic states. Conditionals are substantially different from propositional beliefs and need specific treatment. In this paper, we present a new approach to conditionals, capturing particularly well their dynamic part as revision policies. We thoroughly axiomatize a principle of conditional preservation as an indifference property with respect to conditional structures of worlds. This principle is developed in a semi-quantitative setting, so as to reveal (...)
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  17. Conditional Preservation and Conditional Indifference.Gabriele Kern-Isberner - 2001 - Journal of Applied Non-Classical Logics 11 (1-2):85-106.
    The idea of preserving conditional beliefs emerged recently as a new paradigm apt to guide the revision of epistemic states. Conditionals are substantially different from propositional beliefs and need specific treatment. In this paper, we present a new approach to conditionals, capturing particularly well their dynamic part as revision policies. We thoroughly axiomatize a principle of conditional preservation as an indifference property with respect to conditional structures of worlds. This principle is developed in a semi-quantitative setting, so as to reveal (...)
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  18. Combining Probabilistic Logic Programming with the Power of Maximum Entropy.Gabriele Kern-Isberner & Thomas Lukasiewicz - 2004 - Artificial Intelligence 157 (1-2):139-202.
  19. Characterizing the Principle of Minimum Cross-Entropy Within a Conditional-Logical Framework.Gabriele Kern-Isberner - 1998 - Artificial Intelligence 98 (1-2):169-208.
  20.  8
    Editorial.Gabriele Kern-Isberner & Wilhelm Rödder - 2006 - Logic Journal of the IGPL 14 (3):409-411.
    This special issue “Inferences and Information Processing in a Conditional Framework“ is dedicated to conditionals as central objects for inferencing and information processing. It presents selected revised papers of the Workshop on Conditionals, Information, and Inference, CII'04, held in Ulm, Germany, co-located with the German national conference on AI, KI'2004.Conditional statements If A then B carry a very special kind of information that can not be captured by interpreting them as material implications. Roughly speaking, the premise, A, provides a context (...)
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  21. Ki-2001 Workshop: Uncertainty in Artificial Intellligence. Informatik-Berichte (8/2001).Gabriele Kern-Isberner, Thomas Lukasiewicz & Emil Weydert (eds.) - 2001
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  22. Ki-2001 Workshop: Uncertainty in Artificial Intellligence.Gabriele Kern-Isberner, Thomas Lukasiewicz & Emil Weydert (eds.) - 2001
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  23. Proceedings of Nmr2016.Gabriele Kern-Isberner & Renata Wassermann (eds.) - 2016
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  24.  21
    Reasoning with Imperfect Information and Knowledge.Gabriele Kern-Isberner, Igor Douven, Markus Knauff & Henri Prade - 2017 - Minds and Machines 27 (1):7-9.
  25.  4
    Belief Base Change Operations for Answer Set Programming.Patrick Krümpelmann & Gabriele Kern-Isberner - 2012 - In Luis Farinas del Cerro, Andreas Herzig & Jerome Mengin (eds.), Logics in Artificial Intelligence. Springer. pp. 294--306.
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  26.  7
    A Concept for the Evolution of Relational Probabilistic Belief States and the Computation of Their Changes Under Optimum Entropy Semantics.Nico Potyka, Christoph Beierle & Gabriele Kern-Isberner - 2015 - Journal of Applied Logic 13 (4):414-440.
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  27.  11
    Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples.Henri Prade, Markus Knauff, Igor Douven & Gabriele Kern-Isberner - 2017 - Minds and Machines 27 (1):37-77.
    This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in dynamic (...)
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