ACM Transactions on Database Systems (TODS)
Answer sets for prioritized logic programs
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Preferred answer sets for extended logic programs
Artificial Intelligence
Disjunctive logic programs with inheritance
Proceedings of the 1999 international conference on Logic programming
Prioritized logic programming and its application to commonsense reasoning
Artificial Intelligence
Dynamic Programming in Datalog with Aggregates
IEEE Transactions on Knowledge and Data Engineering
Reasoning with Prioritized Defaults
LPKR '97 Selected papers from the Third International Workshop on Logic Programming and Knowledge Representation
Search and Optimization Problems in Datalog
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Nonmonotonic Reasoning by Monotonic Inference with Priority Constraints
NMELP '96 Selected papers from the Non-Monotonic Extensions of Logic Programming
Alternating Fixpoint Theory for Logic Programs with Priority
CL '00 Proceedings of the First International Conference on Computational Logic
Logic programming with ordered disjunction
Eighteenth national conference on Artificial intelligence
A framework for compiling preferences in logic programs
Theory and Practice of Logic Programming
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A comparative study of logic programs with preference
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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The present work proposes a new semantics for logic program with preference rules and studies logic programs enriched with both aggregates and preference rules. The interest of research literature in handling user preferences to express a partial order on rules and literals is reflected by an extensive number of proposals. The association of aggregates and preferences is, here, used to also express a partial order on global models, other than on literals and rules, so that optimization problems can be expressed in a simple and elegant way. The use of aggregates makes logic languages more flexible and intuitive, without any additional computational complexity.