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Dissertation Abstract: Qualitative Rational Reasoning with Finite Conditional Knowledge Bases

Theoretical and Implementational Aspects

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Abstract

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.

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Correspondence to Christian Eichhorn.

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The thesis was supported by Grant KI1413/5-1 of Deutsche Forschungsgemeinschaft (DFG) to Gabriele Kern-Isberner as part of the priority program “New Frameworks of Rationality” (SPP 1516).

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Eichhorn, C. Dissertation Abstract: Qualitative Rational Reasoning with Finite Conditional Knowledge Bases. Künstl Intell 33, 93–96 (2019). https://doi.org/10.1007/s13218-018-00569-8

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  • DOI: https://doi.org/10.1007/s13218-018-00569-8

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