Reasoning in Non-Probabilistic Uncertainty: Logic Programming and
  Neural-Symbolic Computing as Examples
  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.
