KI - Künstliche Intelligenz

, Volume 33, Issue 1, pp 93–96 | Cite as

Dissertation Abstract: Qualitative Rational Reasoning with Finite Conditional Knowledge Bases

Theoretical and Implementational Aspects
  • Christian EichhornEmail author
Dissertation and Habilitation Abstracts


Inferring prior unknown knowledge from the information given is a core task of the discipline knowledge representation and reasoning in (symbolic) artificial intelligence. With conditionals as building blocks of knowledge bases, inductive methods generate epistemic states on which inference relations are defined. This thesis recalls established approaches to these tasks, and both researches them and compares them based on formal properties. It uses network approaches to make the tasks of generating and storing the results easier, and researches the applicability of formal methods to the results of psychological studies to model human reasoning. In recalling techniques like the so called OCF-networks and Inference Patterns, this dissertation abstract provides a brief view into these topics.


Knowledge representation and reasoning Symbolic AI Conditionals C-representations 

Mathematics Subject Classification

62P15 68T27 68T30 


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Copyright information

© Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.SIT GmbHHemerGermany

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