Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
On the hardness of approximate reasoning
Artificial Intelligence
GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Using caching to solve larger probabilistic planning problems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Initial experiments in stochastic satisfiability
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
A machine program for theorem-proving
Communications of the ACM
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Algorithms and Complexity Results for #SAT and Bayesian Inference
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
New advances in inference by recursive conditioning
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Value elimination: bayesian inference via backtracking search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Heuristics for fast exact model counting
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
On probabilistic inference by weighted model counting
Artificial Intelligence
Learning to assign degrees of belief in relational domains
Machine Learning
Multi-state Directed Acyclic Graphs
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Exploiting Decomposition in Constraint Optimization Problems
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Efficient Genome Wide Tagging by Reduction to SAT
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Logical Compilation of Bayesian Networks with Discrete Variables
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Using Model Counting to Find Optimal Distinguishing Tests
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Artificial General Intelligence through Large-Scale, Multimodal Bayesian Learning
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Online Rule Learning via Weighted Model Counting
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Solving MAP exactly by searching on compiled arithmetic circuits
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
AND/OR Branch-and-Bound search for combinatorial optimization in graphical models
Artificial Intelligence
Memory intensive AND/OR search for combinatorial optimization in graphical models
Artificial Intelligence
Using more reasoning to improve #SAT solving
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Counting models using extension rules
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Exploiting causal independence using weighted model counting
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
An algebraic graphical model for decision with uncertainties, feasibilities, and utilities
Journal of Artificial Intelligence Research
Probabilistic planning via heuristic forward search and weighted model counting
Journal of Artificial Intelligence Research
Solving #SAT and Bayesian inference with backtracking search
Journal of Artificial Intelligence Research
A dynamic approach to MPE and weighted MAX-SAT
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
From sampling to model counting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Variable and value ordering for MPE search
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Compiling relational Bayesian networks for exact inference
International Journal of Approximate Reasoning
Leveraging belief propagation, backtrack search, and statistics for model counting
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Learning to assign degrees of belief in relational domains
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Computing the density of states of Boolean formulas
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Exploiting structure in weighted model counting approaches to probabilistic inference
Journal of Artificial Intelligence Research
Encoding CNFs to empower component analysis
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Functional treewidth: bounding complexity in the presence of functional dependencies
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Importance sampling-based estimation over AND/OR search spaces for graphical models
Artificial Intelligence
An efficient Monte-Carlo algorithm for pricing combinatorial prediction markets for tournaments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Probabilistic symbolic execution
Proceedings of the 2012 International Symposium on Software Testing and Analysis
The Multivariate Algorithmic Revolution and Beyond
Generalized weighted model counting: an efficient monte-carlo meta-algorithm (working paper)
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Knowledge compilation for model counting: affine decision trees
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Artificial Intelligence: From programs to solvers
AI Communications - ECAI 2012 Turing and Anniversary Track
Probabilistic inference with noisy-threshold models based on a CP tensor decomposition
International Journal of Approximate Reasoning
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Over the past decade general satisfiability testing algorithms have proven to be surprisingly effective at solving a wide variety of constraint satisfaction problem, such as planning and scheduling (Kautz and Selman 2003). Solving such NP-complete tasks by "compilation to SAT" has turned out to be an approach that is of both practical and theoretical interest. Recently, (Sang et al. 2004) have shown that state of the art SAT algorithms can be efficiently extended to the harder task of counting the number of models (satisfying assignments) of a formula, by employing a technique called component caching. This paper begins to investigate the question of whether "compilation to model-counting" could be a practical technique for solving real-world #P-complete problems, in particular Bayesian inference. We describe an efficient translation from Bayesian networks to weighted model counting, extend the best model-counting algorithms to weighted model counting, develop an efficient method for computing all marginals in a single counting pass, and evaluate the approach on computationally challenging reasoning problems.