Foundations of logic programming
Foundations of logic programming
Quantitative deduction and its fixpoint theory
Journal of Logic Programming
POPL '87 Proceedings of the 14th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Solving large combinatorial problems in logic programming
Journal of Logic Programming - Logic programming applications
Constraint logic programming languages
Communications of the ACM
A logic language for combinatorial optimization
Annals of Operations Research
A catalog of complexity classes
Handbook of theoretical computer science (vol. A)
Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Probabilistic logic programming
Information and Computation
Probabilistic deductive databases
ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
Disjunctive stable models: unfounded sets, fixpoint semantics, and computation
Information and Computation
ACM Transactions on Database Systems (TODS)
Stable models and non-determinism in logic programs with negation
PODS '90 Proceedings of the ninth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Extending and implementing the stable model semantics
Artificial Intelligence
A Parametric Approach to Deductive Databases with Uncertainty
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Stable Model Checking Made Easy
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Computation of Non-Ground Disjunctive Well-Founded Semantics with Constraint Logic Programming
NMELP '96 Selected papers from the Non-Monotonic Extensions of Logic Programming
Constraint (Logic) Programming: A Survey on Research and Applications
Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
On Indefinite Databases and the Closed World Assumption
Proceedings of the 6th Conference on Automated Deduction
Modeling Uncertainty in Deductive Databases
DEXA '94 Proceedings of the 5th International Conference on Database and Expert Systems Applications
The complexity of relational query languages (Extended Abstract)
STOC '82 Proceedings of the fourteenth annual ACM symposium on Theory of computing
Annals of Mathematics and Artificial Intelligence
Models for incomplete and probabilistic information
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
Focused most probable world computations in probabilistic logic programs
Annals of Mathematics and Artificial Intelligence
Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems
ACM Transactions on Computational Logic (TOCL)
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Abstract--Almost all semantics for logic programs with negation identify a set, SEM(P), of models of program P, as the intended semantics of P, and any model M in this class is considered a possible meaning of P with regard to the semantics the user has in mind. Thus, for example, in the case of stable models [CHECK END OF SENTENCE], choice models [CHECK END OF SENTENCE], answer sets [CHECK END OF SENTENCE], etc., different possible models correspond to different ways of "completing驴 the incomplete information in the logic program. However, different end-users may have different ideas on which of these different models in SEM(P) is a reasonable one from their point of view. For instance, given SEM(P), user U_1 may prefer model M_1\in SEM(P) to model M_2\in SEM(P) based on some evaluation criterion that she has. In this paper, we develop a logic program semantics based on Optimal Models. This semantics does not add yet another semantics to the logic programming arena--it takes as input an existing semantics SEM(P)and a user-specified objective function Obj, and yields a new semantics \underline{{\rm{Opt}}}(P)\subseteq SEM(P) that realizes the objective function within the framework of preferred models identified already by SEM(P). Thus, the user who may or may not know anything about logic programming has considerable flexibility in making the system reflect her own objectives by building "on top驴 of existing semantics known to the system. In addition to the declarative semantics, we provide a complete complexity analysis and algorithms to compute optimal models under varied conditions when SEM(P) is the stable model semantics, the minimal models semantics, and the all-models semantics.