Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples
This work addresses the problem of handling non-probabilistic uncertainty for researchers in AI and logic, offering foundational insights but is incremental in its application of existing logic-based techniques.
The paper argues that probability is not the only approach to uncertainty, highlighting types of uncertainty that cannot be addressed probabilistically, and demonstrates that logic-based methods, such as Logic Programming with Kleene semantics and neural-symbolic implementations of Input/Output logic, can effectively support reasoning under uncertainty.
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.