Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Database System Implementation
Database System Implementation
Logic programs with stable model semantics as a constraint programming paradigm
Annals of Mathematics and Artificial Intelligence
Improving ASP Instantiators by Join-Ordering Methods
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
A Comparison of Different Techniques for Grounding Near-Propositional CNF Formulae
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Extending Classical Logic with Inductive Definitions
CL '00 Proceedings of the First International Conference on Computational Logic
Software Abstractions: Logic, Language, and Analysis
Software Abstractions: Logic, Language, and Analysis
Tools for modeling and solving search problems
AI Communications
Artificial Intelligence
Estimation of the number of tuples satisfying a query expressed in predicate calculus language
VLDB '80 Proceedings of the sixth international conference on Very Large Data Bases - Volume 6
A logic of nonmonotone inductive definitions
ACM Transactions on Computational Logic (TOCL)
Enhancing DLV instantiator by backjumping techniques
Annals of Mathematics and Artificial Intelligence
The Second Answer Set Programming Competition
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Compact propositional encoding of first-order theories
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A framework for representing and solving NP search problems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Grounding for model expansion in k-guarded formulas with inductive definitions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compiling problem specifications into SAT
Artificial Intelligence - Special volume on reformulation
Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Well-founded semantics and the algebraic theory of non-monotone inductive definitions
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
GrinGo: a new grounder for answer set programming
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Kodkod: a relational model finder
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
SAT(ID): satisfiability of propositional logic extended with inductive definitions
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Reducing inductive definitions to propositional satisfiability
ICLP'05 Proceedings of the 21st international conference on Logic Programming
Constraint Propagation for First-Order Logic and Inductive Definitions
ACM Transactions on Computational Logic (TOCL)
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Grounding is the task of reducing a first-order theory and finite domain to an equivalent propositional theory. It is used as preprocessing phase in many logic-based reasoning systems. Such systems provide a rich first-order input language to a user and can rely on efficient propositional solvers to perform the actual reasoning. Besides a first-order theory and finite domain, the input for grounders contains in many applications also additional data. By exploiting this data, the size of the grounder's output can often be reduced significantly. A common practice to improve the efficiency of a grounder in this context is by manually adding semantically redundant information to the input theory, indicating where and when the grounder should exploit the data. In this paper we present a method to compute and add such redundant information automatically. Our method therefore simplifies the task of writing input theories that can be grounded efficiently by current systems. We first present our method for classical first-order logic (FO) theories. Then we extend it to FO(ID), the extension of FO with inductive definitions, which allows for more concise and comprehensive input theories. We discuss implementation issues and experimentally validate the practical applicability of our method.