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Problem instances are those in [19].
Numerical results for a variety of problem instances are reported.
Computational results for several problem instances are reported.
Thus, we obtain a total of 280 problem instances.
This enables its applications on large problem instances.
In experiments, where most probable explanations in Bayesian networks are computed, we use synthetic problem instances as well as problem instances from applications.
We demonstrate the existence of a gap in computational cost between two classes of problem instances.
This region contains a high proportion of relatively hard problem instances.
Constructing plans that can handle multiple problem instances is a longstanding open problem in AI.
This method is simple and able to make use of the sparsity of the problem instances.
Most commonly, problem instances reduced to SAT are solved with a general-purpose SAT solver.
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CEO of Professional Science Editing for Scientists @ prosciediting.com