Transformation Rules for First-Order Probabilistic Conditional Logic Yielding Parametric Uniformity

  • Ruth Janning
  • Christoph Beierle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7006)

Abstract

A major challenge in knowledge representation is to express uncertain knowledge. One possibility is to combine logic and probability. In this paper, we investigate the logic FO-PCL that uses first-order probabilistic conditionals to formulate uncertain knowledge. Reasoning in FO-PCL employs the principle of maximum entropy which in this context refers to the set of all ground instances of the conditionals in a knowledge base \(\mathcal R\). We formalize the syntactic criterion of FO-PCL interactions in \(\mathcal R\) prohibiting the maximum entropy model computation on the level of conditionals instead of their instances. A set of rules is developed transforming \(\mathcal R\) into an equivalent knowledge base \(\mathcal R^{\prime}\) without FO-PCL interactions.

Keywords

Maximum Entropy Transformation Rule Predicate Symbol Constant Symbol Ground Atom 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ruth Janning
    • 1
  • Christoph Beierle
    • 1
  1. 1.Fak. für Mathematik und InformatikFernUniversitätHagenGermany

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