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Scenario-based optimization is a problem class often occurring in finance, planning and control.
The results demonstrated the potential of the inference system to this problem class.
This problem class has applicability in areas such as production and logistics planning and financial engineering.
But the "unknown unknown" is the kind of problem class that can be solved with the next wave.
However, this prediction is associated with a large variance and depends on the somewhat arbitrary choice of the problem class.
Whereas classical learning methods aim at best for a parametric generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class, and to apply generalization on a conceptual level, resulting in new models.
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Going to a selective school will help mute another problem: classes so big it's an ordeal just learning the children's names.
Unsupervised separation of a group of datums of a particular type, into clusters which are homogenous within a problem class-specific context, is a classical research problem which is still actively visited.
This means no more problem classes can be addressed.
We present planning algorithms for these problem classes, and prove that they are correct and complete.
There exists different problem classes based on the size of the problem.
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