Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples |
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| Authors: | Tarek R Besold Artur d’Avila Garcez Keith Stenning Leendert van der Torre Michiel van Lambalgen |
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| Affiliation: | 1.Digital Media Lab, Center for Computing and Communication Technologies (TZI),University of Bremen,Bremen,Germany;2.Department of Computer Science,City University London,London,UK;3.School of Informatics,University of Edinburgh,Edinburgh,Scotland, UK;4.Computer Science and Communication,University of Luxembourg,Esch-sur-Alzette,Luxembourg;5.Faculty of Humanities, Logic and Language,University of Amsterdam,Amsterdam,The Netherlands |
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| Abstract: | 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. |
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