Logo des Repositoriums

Auflistung nach Schlagwort "Conditional logic"

1 - 2 von 2
  • Zeitschriftenartikel
    A Practical Comparison of Qualitative Inferences with Preferred Ranking Models
    (KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Beierle, Christoph; Eichhorn, Christian; Kutsch, Steven
    When 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.
  • Zeitschriftenartikel
    Extending and Completing Probabilistic Knowledge and Beliefs Without Bias
    (KI - Künstliche Intelligenz: Vol. 29, No. 3, 2015) Beierle, Christoph; Kern-Isberner, Gabriele; Finthammer, Marc; Potyka, Nico
    Combining 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.