Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
On the properties of combination set operations
Information Processing Letters
Compiling constraint satisfaction problems
Artificial Intelligence
Indexical-Based Solver Learning
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Automatic Generation of Constraint Propagation Algorithms for Small Finite Domains
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Maintaining Generalized Arc Consistency on Ad-hoc n-ary Boolean Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
H-DPOP: using hard constraints for search space pruning in DCOP
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
M-DPOP: faithful distributed implementation of efficient social choice problems
Journal of Artificial Intelligence Research
Small formulas for large programs: on-line constraint simplification in scalable static analysis
SAS'10 Proceedings of the 17th international conference on Static analysis
Towards "propagation = logic + control"
ICLP'06 Proceedings of the 22nd international conference on Logic Programming
Dynamic multiagent load balancing using distributed constraint optimization techniques
Web Intelligence and Agent Systems
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A general n-ary constraint is usually represented explicitly as a set of its solution tuples, which may need exponential space. In this paper, we introduce a new representation for general n-ary constraints called Constrained Decision Diagram (CDD). CDD generalizes BDD-style representations and the main feature is that it combines constraint reasoning/consistency techniques with a compact data structure. We present an application of CDD for recording all solutions of a conjunction of constraints. Instead of an explicit representation, we can implicitly encode the solutions by means of constraint propagation. Our experiments confirm the scalability and demonstrate that CDDs can drastically reduce the space needed over explicit and ZBDD representations.