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Systems and Implementations for Solving Reasoning Problems in Conditional Logics

  • Christoph BeierleEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9616)

Abstract

Default rules like “If A, then normally B” or probabilistic rules like “If A, then B with probability x” are powerful constructs for knowledge representation. Such rules can be formalized as conditionals, denoted by \((B|A)\) or \((B|A)[x]\), and a conditional knowledge base consists of a set of conditionals. Different semantical models have been proposed for conditional knowledge bases, and the most important reasoning problems are to determine whether a knowledge base is consistent and to determine what a knowledge base entails. We present an overview on systems and implementations our group has been working on for solving reasoning problems in various semantics that have been developed for conditional knowledge bases. These semantics include quantitative, semi-quantitative, and qualitative conditional logics, based on both propositional logic and on first-order logic.

Keywords

Ranking Function Constraint Satisfaction Problem Epistemic State Reasoning Task Default Rule 
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.

Notes

Acknowledgments

A large part of the work reported here was done in cooperation and in joint projects with Gabriele Kern-Isberner and her research group at TU Dortmund University, Germany. I am also very grateful to all members of the project teams involved, in particular to Marc Finthammer, Jens Fisseler, Nico Potyka, Matthias Thimm, and numerous students for their contributions.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Mathematics and Computer ScienceUniversity of HagenHagenGermany

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