Reference : Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural Symbolic Com...

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/30739

Title :	Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural Symbolic Computing as Examples
Language :	English
Author, co-author :	Besold, Tarek [University of Bremen > Digital Media Lab]
	Garcez, Artur d'Avila [City, University of London > Department of Computer Science]
	Stenning, Keith [University of Edinburgh > School of Informatics]
	van der Torre, Leon [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	van Lambalgen, Michiel [University of Amsterdam > Faculty of Humanities > Logic and Language]
Publication date :	9-Mar-2017
Journal title :	Minds and Machines
Peer reviewed :	Yes
Audience :	International
Keywords :	[en] Uncertainty in reasoning ; Interpretation ; Logic programming ; Dynamic norms ; Neural-symbolic integration
Abstract :	[en] This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in dynamic normative contexts.
Permalink :	http://hdl.handle.net/10993/30739

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