Abstract
Intentional forgetting means to deliberately give up information and is a crucial part of change or consolidation processes, or to make knowledge more compact. Two well-known forgetting operations are contraction in the AGM theory of belief change, and various types of variable elimination in logic programming. While previous work dealt with postulates being inspired from logic programming, in this paper we focus on evaluating forgetting in epistemic states according to postulates coming from AGM belief change theory. We consider different forms of contraction, marginalization, and conditionalization as major representatives of forgetting operators to be evaluated. We use Spohn’s ranking functions as a common semantic base to show that all operations can be realized in one logical framework, thereby exploring the richness of forgetting operations in a comparable way.
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Acknowledgements
The research reported here was supported by the German Research Society (DFG) within the Priority Research Program Intentional Forgetting in Organisations (DFG-SPP 1921; grants BE 1700/9-1, KE 1413/10-1).
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Kern-Isberner, G., Bock, T., Sauerwald, K., Beierle, C. (2019). Belief Change Properties of Forgetting Operations over Ranking Functions. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_37
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