Reference : Revising Incompletely Specified Convex Probabilistic Belief Bases

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/30343

Title :	Revising Incompletely Specified Convex Probabilistic Belief Bases
Language :	English
Author, co-author :	Rens, Gavin [University of KwaZulu-Natal > School of Mathematics, Statistics and Computational Sciences > > Postdoc]
	Meyer, Thomas [University of Cape Town > Department of Computer Science > > Full professor]
	Casini, Giovanni [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Apr-2016
Main document title :	Proceedings of the 16th International Workshop on Non-Monotonic Reasoning (NMR 2016)
Editor :	Kern-Isberner,, Gabriele
	Wassermann, Renata
Publisher :	Technische Universität Dortmund
Pages :	133-142
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	16th International Workshop on Non-Monotonic Reasoning (NMR 2016)
Event date :	from 22-04-2016 to 24-04-2016
Event place (city) :	Cape Town
Event country :	South Africa
Keywords :	[en] probability ; belief revision
Abstract :	[en] We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy method are reasonable, yet yield different results.
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	SOUL (AFR/9181001)
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/30343
FnR project :	FnR ; FNR9181001 > Giovanni Casini > SOUL > Subjective and Objective Uncertainty in Description Logics > 01/07/2015 > 30/06/2017 > 2014

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