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Building a knowledge base system for an integration of logic programming and classical logic KU Leuven
This paper presents a Knowledge Base project for FO(ID), an extension of classical logic with inductive definitions. This logic is a natural integration of classical logic and logic programming based on the view of a logic program as a definition. We discuss the relationship between inductive definitions and common sense reasoning and the strong similarities and striking differences with ASP and Abductive LP. We report on inference systems that ...
Fuzzy autoepistemic logic and its relation to fuzzy answer set programming Vrije Universiteit Brussel
Investigating the relation between fuzzy autoepistemic logic and fuzzy answer set programming
Neural probabilistic logic programming in DeepProbLog Ghent University KU Leuven
Heuristics entwined with handlers combined: from functional specification to logic programming implementation Ghent University
A long-standing problem in logic programming is how to cleanly separate logic and control. While solutions exist, they fall short in one of two ways: some are too intrusive, because they require significant changes to PrologU+2019s underlying implementation; others are lacking a clean semantic grounding. We resolve both of these issues in this paper. We derive a solution that is both lightweight and principled. We do so by starting from a ...
Towards flexible goal-oriented logic programming Ghent University
The functional perspective on advanced logic programming KU Leuven
The basics of logic programming, as embodied by Prolog, are generally well-known in the programming language community. However, more advanced techniques, such as tabling, answer subsumption and probabilistic logic programming fail to attract the attention of a larger audience. The cause for the community's seemingly limited interest lies with the presentation of these features: the literature frequently focuses on implementations and examples ...
On the relationship between logical Bayesian networks and probabilistic logic programming based on the distribution semantics KU Leuven
A significant part of current research on (inductive) logic programming deals with probabilistic logical models. Over the last decade many logics or languages for representing such models have been introduced. There is currently a great need for insight into the relationships between all these languages. One kind of languages are those that extend probabilistic models with elements of logic, such as the language of Logical Bayesian Networks ...