Edge-valued binary decision diagrams for multi-level hierarchical verification
DAC '92 Proceedings of the 29th ACM/IEEE Design Automation Conference
Algebraic decision diagrams and their applications
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Consistency restoriation and explanations in dynamic CSPs----application to configuration
Artificial Intelligence
Factored Edge-Valued Binary Decision Diagrams
Formal Methods in System Design
Formal Verification Using Edge-Valued Binary Decision Diagrams
IEEE Transactions on Computers
Arc consistency for soft constraints
Artificial Intelligence
Journal of Artificial Intelligence Research
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
The comparative linguistics of knowledge representation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Decision diagrams for the computation of semiring valuations
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SDD: a new canonical representation of propositional knowledge bases
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Existing languages in the valued decision diagrams (VDDs) family, including ADD, AADD, and those of the SLDD family, prove to be valuable target languages for compiling multivariate functions. However, their efficiency is directly related to the size of the compiled formulae. In practice, the existence of canonical forms may have a major impact on the size of the compiled VDDs. While efficient normalization procedures have been pointed out for ADD and AADD the canonicity issue for SLDD formulae has not been addressed so far. In this paper, the SLDD family is revisited. We modify the algebraic requirements imposed on the valuation structure so as to ensure tractable conditioning, optimization and normalization for some languages of the revisited SLDD family. We show that AADD is captured by this family. Finally, we compare the spatial efficiency of some languages of this family, from both the theoretical side and the practical side.