Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Readings in nonmonotonic reasoning
A semantical approach to nonmonotonic logics
Readings in nonmonotonic reasoning
Bilattices and the semantics of logic programming
Journal of Logic Programming
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Strongly equivalent logic programs
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Handbook of Automated Reasoning: Volume 1
Handbook of Automated Reasoning: Volume 1
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Characterizations of the Disjunctive Well-Founded Semantics: Confluent Calculi and Iterated GCWA
Journal of Automated Reasoning
Characterizations of the Stable Semantics by Partial Evaluation
LPNMR '95 Proceedings of the Third International Conference on Logic Programming and Nonmonotonic Reasoning
Equivalence in Answer Set Programming
LOPSTR '01 Selected papers from the 11th International Workshop on Logic Based Program Synthesis and Transformation
Combining probabilistic logic programming with the power of maximum entropy
Artificial Intelligence - Special issue on nonmonotonic reasoning
POLICY '05 Proceedings of the Sixth IEEE International Workshop on Policies for Distributed Systems and Networks
Possibilistic uncertainty handling for answer set programming
Annals of Mathematics and Artificial Intelligence
An introduction to fuzzy answer set programming
Annals of Mathematics and Artificial Intelligence
Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming
Strong equivalence for logic programs with preferences
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Pstable Semantics for Logic Programs with Possibilistic Ordered Disjunction
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Logic programming and nonmonotonic reasoning: from theory to systems and applications
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Semantics for possibilistic disjunctive programs
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Pstable semantics for possibilistic logic programs
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
LPOD answer sets and nash equilibria
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Preferences in AI: An overview
Artificial Intelligence
Answer set programming for computing decisions under uncertainty
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Nested Preferences in Answer Set Programming
Fundamenta Informaticae - Latin American Workshop on Logic Languages, Algorithms and New Methods of Reasoning (LANMR)
Dealing with explicit preferences and uncertainty in answer set programming
Annals of Mathematics and Artificial Intelligence
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Logic programs with ordered disjunction (or LPODs) have shown to be a flexible specification language able to model and reason about preferences in a natural way. However, in some realistic applications which use user preferences in the reasoning, information can be pervaded with vagueness and a preference-aware reasoning process that can handle uncertainty is required. In this paper we address these issues, and we propose a framework which combines LPODs and possibilistic logic to be able to deal with a reasoning process that is preference-aware, non-monotonic, and uncertain. We define a possibilistic semantics for capturing logic programs with possibilistic ordered disjunction (or LPPODs) which is a generalization of the original semantics. Moreover, we present several transformation rules which can be used to optimize LPODs and LPPODs code and we show how the semantics of LPODs and the possibilistic semantics of LPPODs are invariant w.r.t. these transformations.