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Explaining Black-Box Models Using Interpretable Surrogates Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Belief Change Properties of Forgetting Operations over Ranking Functions

Authors : Gabriele Kern-Isberner, Tanja Bock, Kai Sauerwald, Christoph Beierle

Published in: PRICAI 2019: Trends in Artificial Intelligence

Publisher: Springer International Publishing

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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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Metadata
Title
Belief Change Properties of Forgetting Operations over Ranking Functions
Authors
Gabriele Kern-Isberner
Tanja Bock
Kai Sauerwald
Christoph Beierle
Copyright Year
2019
Book
PRICAI 2019: Trends in Artificial Intelligence

Print ISBN: 978-3-030-29907-1

Electronic ISBN: 978-3-030-29908-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-29908-8_37

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