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

By Tarek R. Besold, A.S. d'Avila Garcez, Keith Stenning, Leendert van der Torre, Michiel van Lambalgen. 2017

25 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Prompting & Reasoning, Knowledge Graphs & Representation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tarek R. Besold
2. Roberto Confalonieri
3. Benedikt Wagner
4. Ludovik Çoba

In Knowledge Graphs & Representation

1. Tarek R. Besold
2. Roberto Confalonieri
3. Benedikt Wagner
4. Ludovik Çoba

Citations in the corpus

• A historical perspective of explainable Artificial Intelligence (2020, background): “…the embedding of symbolic (expert) knowledge into the sub-symbolic model as well as for the extraction of learned and refined knowledge from the connectionist model, which can drive the data-based modification and fine-tuning of predefined rules (see e.g., Besold, Garcez, Stenning, et al., 2017).”