KI - Künstliche Intelligenz

, Volume 29, Issue 3, pp 279–289

Qualitative and Semi-Quantitative Inductive Reasoning with Conditionals

Technical Project Report
Research Project

DOI: 10.1007/s13218-015-0376-x

Cite this article as:
Eichhorn, C. & Kern-Isberner, G. Künstl Intell (2015) 29: 279. doi:10.1007/s13218-015-0376-x


Conditionals like “birds fly—if bird then fly” are crucial for commonsense reasoning. In this technical project report we show that conditional logics provide a powerful formal framework that helps understanding if-then sentences in a way that is much closer to human reasoning than classical logic and allows for high-quality reasoning methods. We describe methods that inductively generate models from conditional knowledge bases. For this, we use both qualitative (like preferential models) and semi-quantitative (like Spohn’s ranking functions) semantics. We show similarities and differences between the resulting inference relations with respect to formal properties. We further report on two graphical methods on top of the ranking approaches which allow to decompose the models into smaller, more feasible components and allow for local inferences.


Conditionals Nonmonotonic reasoning Induction  System P Networks Ordinal conditional function Conditional structures 

Funding information

Funder NameGrant NumberFunding Note
  • KI 1413/5-1

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Technische Universität DortmundDortmundGermany

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