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. & van Lambalgen, M. (2017). Reasoning in non-probabilistic uncertainty: logic programming and neural-symbolic computing as examples. Minds and Machines, 27(1), pp. 37-77. 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: | Uncertainty in reasoning, Interpretation, Logic programming, Dynamic norms, Neural-symbolic integration |
| Departments: | School of Science & Technology > Computer Science |
| SWORD Depositor: |
Download (959kB) | Preview
Export
Downloads
Downloads per month over past year
Metadata
MetadataActions (login required)
Admin Login