An empirical evaluation of knowledge compilation by theory approximation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Unit Refutations and Horn Sets
Journal of the ACM (JACM)
Renaming a Set of Clauses as a Horn Set
Journal of the ACM (JACM)
Semantical and computational aspects of horn approximations
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An analysis of approximate knowledge compilation
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Knowledge compilation using theory prime implicates
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Compilation for critically constrained knowledge bases
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
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Propositional lower bounds: Algorithms and complexity
Annals of Mathematics and Artificial Intelligence
Approximation of Relations by Propositional Formulas: Complexity and Semantics
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Disjunctions of Horn Theories and Their Cores
ISAAC '98 Proceedings of the 9th International Symposium on Algorithms and Computation
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New Results for Horn Cores and Envelopes of Horn Disjunctions
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IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
First order LUB approximations: characterization and algorithms
Artificial Intelligence - Special volume on reformulation
On the diffvierence of horn theories
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Horn upper bounds and renaming
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Information loss in knowledge compilation: A comparison of Boolean envelopes
Artificial Intelligence
Belief revision within fragments of propositional logic
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One of the obstacles to the effective compilation of propositional knowledge bases (KBs) using Hom approximations, as introduced by (Selman & Kautz 1991), is the lack of computationally feasible methods for generating Hom bounds. In this paper new algorithms for generating Hom Greatest Lower Bounds (GLB) that can apply to large size KBs, are presented. The approach is extended through a more general target language: the renamable Hom class. The conditions under which a renamable Hom formula is a renamable Hom GLB of a KB are established and algorithms for computing it are derived. These algorithms can be used in the other approaches based on computation of Hom or renamable lower bounds as (Boufkhad et al. 1997). The efficiency of these algorithms and the tightness with respect to the KB in terms of number of models of the bounds, are experimentally evaluated. The renamable Hom GLB proves to be closer to the KB than the Horn GLB.