The paper is originally published in the Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (ISBN: 978-0-9992411-0-3), published by International Joint Conferences on Artificial Intelligence. The original publication is available at https://www.ijcai.org/proceedings/2017/129.
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[en] Belief change and non-monotonic reasoning are usually viewed as two sides of the same coin,
with results showing that one can formally be defined in terms of the other. In this paper we show that we can also integrate the two formalisms by studying belief change within a (preferential) non-monotonic framework. This integration relies heavily on the identification of the monotonic core of a non-monotonic framework. We consider belief change operators in a non-monotonic propositional
setting with a view towards preserving consistency. These results can also be applied to the preservation of coherence—an important notion within the field of logic-based ontologies. We show that the standard AGM approach to belief change can be adapted to a preferential non-monotonic framework, with the definition of expansion, contraction, and revision operators, and corresponding representation results. Surprisingly, preferential AGM belief change, as defined here, can be obtained in terms of classical AGM belief change.