MINION: A Fast, Scalable, Constraint Solver
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Cardinality Networks and Their Applications
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Efficient haplotype inference with boolean satisfiability
AAAI'06 Proceedings of the 21st 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
The design of ESSENCE: a constraint language for specifying combinatorial problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Grounding for model expansion in k-guarded formulas with inductive definitions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
MiniZinc: towards a standard CP modelling language
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Enfragmo: a system for modelling and solving search problems with logic
LPAR'12 Proceedings of the 18th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
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Given a finite domain, grounding is the the process of creating a variable-free first-order formula equivalent to a first-order sentence. As the firstorder sentences can be used to describe a combinatorial search problem, efficient grounding algorithms would help in solving such problems effectively and makes advanced solver technology (such as SAT) accessible to a wider variety of users. One promising method for grounding is based on the relational algebra from the field of Database research. In this paper, we describe the extension of this method to ground formulas of first-order logic extended with arithmetic, expansion functions and aggregate operators. Our method allows choice of particular CNF representations for complex constraints, easily.