Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Efficient implementation of a BDD package
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Shared binary decision diagram with attributed edges for efficient Boolean function manipulation
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Learning Conjunctions of Horn Clauses
Machine Learning - Computational learning theory
Horn approximations of empirical data
Artificial Intelligence
Exact learning Boolean functions via the monotone theory
Information and Computation
Artificial Intelligence
Doing two-level logic minimization 100 times faster
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
Optimizing OBDDs Is Still Intractable for Monotone Functions
MFCS '98 Proceedings of the 23rd International Symposium on Mathematical Foundations of Computer Science
Ordered Binary Decision Diagrams as Knowledge-Bases
ISAAC '99 Proceedings of the 10th International Symposium on Algorithms and Computation
Translating between Horn representations and their characteristic models
Journal of Artificial Intelligence Research
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We consider translation among conjunctive normal forms (CNFs), characteristic models, and ordered binary decision diagrams (OBDDs) of Boolean functions. It is shown in this paper that Horn OBDDs can be translated into their CNFs in polynomial time. As for the opposite direction, the problem can be solved in polynomial time if the ordering of variables in the resulting OBDD is specified as an input. In case that such ordering is not specified and the resulting OBDD must be of minimum size, its decision version becomes NP-complete. Similar results are also obtained for the translation in both directions between characteristic models and OBDDs. We emphasize here that the above results holds on any class of functions having a basis B with |B| = d.