A non-ground realization of the stable and well-founded semantics
Theoretical Computer Science
Extending and implementing the stable model semantics
Artificial Intelligence
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Logic programs with stable model semantics as a constraint programming paradigm
Annals of Mathematics and Artificial Intelligence
Omega-Restricted Logic Programs
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Computing Non-Ground Representations of Stable Models
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
ASSAT: computing answer sets of a logic program by SAT solvers
Artificial Intelligence - Special issue on nonmonotonic reasoning
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Answer Set Programming Based on Propositional Satisfiability
Journal of Automated Reasoning
Experimenting with parallelism for the instantiation of ASP programs
Journal of Algorithms
The First Version of a New ASP Solver: ASPeRiX
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
A new perspective on stable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Conflict-driven answer set solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
From answer set logic programming to circumscription via logic of GK
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
GrinGo: a new grounder for answer set programming
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Logic programs with abstract constraint atoms: the role of computations
ICLP'07 Proceedings of the 23rd international conference on Logic programming
Pbmodels: software to compute stable models by pseudoboolean solvers
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
The First Version of a New ASP Solver: ASPeRiX
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Compiling answer set programs into event-driven action rules
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Underwater archaeological 3D surveys validation within the removed sets framework
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Answer set modules for logical agents
Datalog'10 Proceedings of the First international conference on Datalog Reloaded
OMiGA: an open minded grounding on-the-fly answer set solver
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
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The natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a Constraint Satisfaction Problem is to elaborate a first order logic program with default negation. In a preliminary step this program, with variables, is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation. In this work, our major contribution is to introduce a new approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. Our methodology applies a forward chaining of first order rules that are grounded on the fly by means of previously produced constants. We have implemented this strategy in our new ASP solver ASPeRiX . The first benefit of our work is to avoid the bottleneck of instantiation phase arising for some problems because of the huge amount of memory needed to ground all rules of a program, even if these rules are not really useful in certain cases. The second benefit is to make the treatment of function symbols easier and without syntactic restriction provided that rules are safe.