Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Resolution versus Search: Two Strategies for SAT
Journal of Automated Reasoning
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Mixtures of deterministic-probabilistic networks and their AND/OR search space
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Cycle-cutset sampling for Bayesian networks
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Indexing correlated probabilistic databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Memory intensive branch-and-bound search for graphical models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Memory intensive AND/OR search for combinatorial optimization in graphical models
Artificial Intelligence
Solving #SAT and Bayesian inference with backtracking search
Journal of Artificial Intelligence Research
MB-DPOP: a new memory-bounded algorithm for distributed optimization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A comparison of time-space schemes for graphical models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Concurrent forward bounding for distributed constraint optimization problems
Artificial Intelligence
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Cutset conditioning is one of the methods of solving reasoning tasks for graphical models, especially when space restrictions make inference (e.g., jointree-clustering) algorithms infeasible. The w-cutset is a natural extension of the method to a hybrid algorithm that performs search on the conditioning variables and inference on the remaining problems of induced width bounded by w. This paper takes a fresh look at these methods through the spectrum of AND/OR search spaces for graphical models. The resulting AND/OR cutset method is a strict improvement over the traditional one, often by exponential amounts.