A machine program for theorem-proving
Communications of the ACM
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Counting Models Using Connected Components
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Journal of Artificial Intelligence Research
From sampling to model counting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A new algorithm for sampling CSP solutions uniformly at random
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
A new approach to model counting
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Importance sampling algorithms for Bayesian networks: Principles and performance
Mathematical and Computer Modelling: An International Journal
Approximate Solution Sampling (and Counting) on AND/OR Spaces
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Refined Bounds for Instance-Based Search Complexity of Counting and Other #P Problems
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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
Exploiting problem structure for solution counting
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Computing the density of states of Boolean formulas
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
SampleSearch: Importance sampling in presence of determinism
Artificial Intelligence
Importance sampling on Bayesian networks with deterministic causalities
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Importance sampling-based estimation over AND/OR search spaces for graphical models
Artificial Intelligence
A flat histogram method for computing the density of states of combinatorial problems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Deriving Information from Sampling and Diving
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
| Hi-index | 0.00 |
We present a new estimator for counting the number of solutions of a Boolean satisfiability problem as a part of an importance sampling framework. The estimator uses the recently introduced SampleSearch scheme that is designed to overcome the rejection problem associated with distributions having a substantial amount of determinism. We show here that the sampling distribution of SampleSearch can be characterized as the backtrack-free distribution and propose several schemes for its computation. This allows integrating Sample-Search into the importance sampling framework for approximating the number of solutions and also allows using Sample-Search for computing a lower bound measure on the number of solutions. Our empirical evaluation demonstrates the superiority of our new approximate counting schemes against recent competing approaches.