Introduction to Digital Logic Design
Introduction to Digital Logic Design
Counting Models Using Connected Components
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A compiler for deterministic, decomposable negation normal form
Eighteenth national conference on Artificial intelligence
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
DPLL with a trace: from SAT to knowledge compilation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
On probabilistic inference by weighted model counting
Artificial Intelligence
Using more reasoning to improve #SAT solving
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Solving #SAT and Bayesian inference with backtracking search
Journal of Artificial Intelligence Research
Journal of Automated Reasoning
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Recent algorithms for model counting and compilation work by decomposing a CNF into syntactically independent components through variable splitting, and then solving the components recursively and independently. In this paper, we observe that syntactic component analysis can miss decomposition opportunities because the syntax may hide existing semantic independence, leading to unnecessary variable splitting. Moreover, we show that by applying a limited resolution strategy to the CNF prior to inference, one can transform the CNF to syntactically reveal such semantic independence. We describe a general resolution strategy for this purpose, and a more specific one that utilizes problem–specific structure. We apply our proposed techniques to CNF encodings of Bayesian networks, which can be used to answer probabilistic queries through weighted model counting and/or knowledge compilation. Experimental results demonstrate that our proposed techniques can have a large effect on the efficiency of inference, reducing time and space requirements significantly, and allowing inference to be performed on many CNFs that exhausted resources previously.