Abstract: Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the ...

Inductive logic programming (ILP) studies the learning of (Prolog) logic programs and other relational knowledge from examples. Most machine learning algorithms are restricted to finite, propositional ...

🧠 Prolog & AI Logic Programming Examples This repository contains a curated collection of Prolog programs that demonstrate the power of logic programming in the domain of Artificial Intelligence (AI) ...

Inductive logic programming (ILP) and machine learning together represent a powerful synthesis of symbolic reasoning and statistical inference. ILP focuses on deriving interpretable logic rules from ...

In order to respond to one of the main challenges of Artificial Intelligence (AI), that is, the effective integration of learning and reasoning, both symbolic inference and statistical learning need ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

Abstract: The notion of assumption-based framework generalises and refines the use of abduction to give a formalisation of non-monotonic reasoning. In this framework, a sentence is a non-monotonic ...