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Consolidating Probabilistic Knowledge Bases via Belief Contraction

Author(s):
De Bona, Glauber ; Finger, Marcelo ; Ribeiro, Marcio M. ; Santos, Yuri D. ; Wassermann, Renata ; AAAI
Total Authors: 6
Document type: Journal article
Source: 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022); v. N/A, p. 10-pg., 2016-01-01.
Abstract

This paper is set to study the applicability of AGM-like operations to probabilistic bases. We focus on the problem of consistency restoration, also called consolidation or contraction by falsity. We aim to identify the reasons why the set of AGM postulates based on discrete operations of deletions and accretions is too coarse to treat finely adjustable probabilistic formulas. We propose new principles that allow one to deal with the consolidation of inconsistent probabilistic bases, presenting a finer method called liftable contraction. Furthermore, we show that existing methods for probabilistic consolidation via distance minimization are particular cases of the methods proposed. (AU)

FAPESP's process: 15/21880-4 - PROVERBS -- PRobabilistic OVERconstrained Boolean Systems: reasoning tools and applications
Grantee:Marcelo Finger
Support Opportunities: Regular Research Grants