Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples
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.
Cite
@article{arxiv.1701.05226,
title = {Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples},
author = {Tarek R. Besold and Artur d'Avila Garcez and Keith Stenning and Leendert van der Torre and Michiel van Lambalgen},
journal= {arXiv preprint arXiv:1701.05226},
year = {2017}
}
Comments
Forthcoming with DOI 10.1007/s11023-017-9428-3 in the Special Issue "Reasoning with Imperfect Information and Knowledge" of Minds and Machines (2017). The final publication will be available at http://link.springer.com. --- Changes to previous version: Fixed some typos and a broken reference