Semantics for possibilistic disjunctive programs

Authors:
Juan Carlos Nieves;Mauricio Osorio;Ulises Cortés
Affiliations:
Universitat Politècnica de Catalunya, Software Department, LSI, Barcelona, Spain;Universidad de las Américas - Puebla Centia, Puebla, México;Universitat Politècnica de Catalunya, Software Department, LSI, Barcelona, Spain
Venue:
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Year:
2007

Citing 5
Cited 7

Possibilistic logic

Handbook of logic in artificial intelligence and logic programming (vol. 3)
Knowledge Representation, Reasoning, and Declarative Problem Solving

Knowledge Representation, Reasoning, and Declarative Problem Solving
Reasoning about Uncertainty

Reasoning about Uncertainty
Possibilistic uncertainty handling for answer set programming

Annals of Mathematics and Artificial Intelligence
Increasing Human-Organ Transplant Availability: Argumentation-Based Agent Deliberation

IEEE Intelligent Systems

Reasoning about actions under uncertainty: A possibilistic approach

Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Possibilistic Well-Founded Semantics

MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Pstable semantics for possibilistic logic programs

MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Supporting decision making in urban wastewater systems using a knowledge-based approach

Environmental Modelling & Software
Weak and strong disjunction in possibilistic ASP

SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Possibilistic semantics for logic programs with ordered disjunction

FoIKS'10 Proceedings of the 6th international conference on Foundations of Information and Knowledge Systems
Semantics for possibilistic answer set programs: Uncertain rules versus rules with uncertain conclusions

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper by considering answer set programming approach and some basic ideas from possibilistic logic, we introduce a possibilistic disjunctive logic programming approach able to deal with reasoning under uncertain and incomplete information. Our approach permits to use explicitly labels like possible, probable, plausible, etc., for capturing the incomplete state of a belief in a disjunctive logic program.