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.
Disciplines :
Computer science
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
External co-authors :
yes
Language :
English
Title :
Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural Symbolic Computing as Examples