Efficient algorithms for combinatorial problems on graphs with bounded, decomposability—a survey
BIT - Ellis Horwood series in artificial intelligence
Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Tree clustering for constraint networks (research note)
Artificial Intelligence
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Experimental evaluation of preprocessing algorithms for constraint satisfaction problems
Artificial Intelligence
Nonserial Dynamic Programming
An improved constraint-propagation algorithm for diagnosis
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Diagnosing tree-decomposable circuits
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Topological parameters for time-space tradeoff
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Decomposable negation normal form
Journal of the ACM (JACM)
Resolution versus Search: Two Strategies for SAT
Journal of Automated Reasoning
Using Recursive Decomposition to Construct Elimination Orders, Jointrees, and Dtrees
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Unifying tree decompositions for reasoning in graphical models
Artificial Intelligence
Optimizing mpf queries: decision support and probabilistic inference
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Unifying tree decompositions for reasoning in graphical models
Artificial Intelligence
Topological parameters for time-space tradeoff
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Many algorithms for processing probabilistic networks are dependent on the topological properties of the problem's structure. Such algorithms (e.g., clustering, conditioning) are effective only if the problem has a sparse graph captured by parameters such as tree width and cycle-outset size. In this paper we initiate a study to determine the potential of structure-based algorithms in real-life applications. We analyze empirically the structural properties of problems coming from the circuit diagnosis domain. Specifically, we locate those properties that capture the effectiveness of clustering and conditioning as well as of a family of conditioning+clustering algorithms designed to gradually trade space for time. We perform our analysis on 11 benchmark circuits widely used in the testing community. We also report on the effect of ordering heuristics on tree-clustering and show that, on our benchmarks, the wellknown max-cardinality ordering is substantially inferior to an ordering called raindegree.