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Reasoning in non-probabilistic uncertainty: logic programming and neural-symbolic computing as examples

Besold, T. R., Garcez, A.D, Stenning, K., van der Torre, L. and van Lambalgen, M. (2017). Reasoning in non-probabilistic uncertainty: logic programming and neural-symbolic computing as examples. Minds and Machines, 27(1), doi: 10.1007/s11023-017-9428-3

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

Publication Type: Article
Publisher Keywords: 08 Information And Computing Sciences, 17 Psychology And Cognitive Sciences, 22 Philosophy And Religious Studies
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: http://openaccess.city.ac.uk/id/eprint/18646
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