Extending the Smodels system with cardinality and weight constraints
Logic-based artificial intelligence
Extending and implementing the stable model semantics
Artificial Intelligence
ASSAT: computing answer sets of a logic program by SAT solvers
Artificial Intelligence - Special issue on nonmonotonic reasoning
Weight constraints as nested expressions
Theory and Practice of Logic Programming
Answer Set Programming Based on Propositional Satisfiability
Journal of Automated Reasoning
Logic programs with monotone abstract constraint atoms*
Theory and Practice of Logic Programming
Integrating answer set programming and constraint logic programming
Annals of Mathematics and Artificial Intelligence
Loop formulas for logic programs with arbitrary constraint atoms
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Properties and applications of programs with monotone and convex constraints
Journal of Artificial Intelligence Research
A new perspective on stable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On finitely recursive programs
ICLP'07 Proceedings of the 23rd international conference on Logic programming
FDNC: decidable non-monotonic disjunctive logic programs with function symbols
LPAR'07 Proceedings of the 14th international conference on Logic for programming, artificial intelligence and reasoning
Answer sets for propositional theories
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
Level Mapping Induced Loop Formulas for Weight Constraint and Aggregate Logic Programs
Fundamenta Informaticae
Level Mapping Induced Loop Formulas for Weight Constraint and Aggregate Logic Programs
Fundamenta Informaticae
Weight constraint programs with evaluable functions
Annals of Mathematics and Artificial Intelligence
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In this paper we consider a new class of logic programs, called weight constraint programs with functions, which are lparse programs incorporating functions over non-Herbrand domains. We define answer sets for these programs and develop a computational mechanism based on loop completion. We present our results in two stages. First, we formulate loop formulas for lparse programs (without functions). Our result improves the previous formulations in that our loop formulas do not introduce new propositional variables, nor there is a need of translating lparse programs to nested expressions. Building upon this result we extend the work to weight constraint programs with functions. We show that the loop completion of such a program can be transformed to a Constraint Satisfaction Problem (CSP) whose solutions correspond to the answer sets of the program, hence off-the-shelf CSP solvers can be used for answer set computation. We show some preliminary experimental results.