Foundations of logic programming
Foundations of logic programming
Quantitative deduction and its fixpoint theory
Journal of Logic Programming
Paraconsistent logic programming
Theoretical Computer Science
Logic programs with classical negation
Logic programming
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Foundations of disjunctive logic programming
Foundations of disjunctive logic programming
Probabilistic logic programming
Information and Computation
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
The Semantics of Predicate Logic as a Programming Language
Journal of the ACM (JACM)
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Complexity and Expressive Power of Logic Programming
CCC '97 Proceedings of the 12th Annual IEEE Conference on Computational Complexity
Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming
Logic programs with uncertainties: a tool for implementing rule-based systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Potassco: The Potsdam Answer Set Solving Collection
AI Communications - Answer Set Programming
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
| Hi-index | 0.00 |
In recent years Logic programming based languages and features-such as rules and non-monotonic constructs-have become important in various knowledge representation paradigms. While the early logic programming languages, such as Horn logic programs and Prolog did not focus on expressing and reasoning with uncertainty, in recent years logic programming languages have been developed that can express both logical and quantitative uncertainty. In this paper we give an overview of such languages and the kind of uncertainty they can express and reason with. Among those, we slightly elaborate on the language P-log that not only accommodates probabilistic reasoning, but also respects causality and distinguishes observational and action updates.