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We present a compiler that translates a problem specification into a propositional satisfiability test (SAT).
We propose a concrete scheme for compiling landmarks into the problem specification.
It can be understood as a four-stage process: problem specification, looking for potential solutions, piloting those solutions, and scaling them where appropriate.
It shows how the theory and the problem specification can be expressed in a first-order language; and demonstrates that this inference and other similar inferences can be justified as deductive conclusions from the theory and the problem specification.
If you disregard these instructions, you are likely to lose points, either for not meeting the problem specification or for errors introduced when attempting a more difficult alternative.
This requires various kinds of transformation applied to the original problem specification as well as to intermediate solutions.
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Mathematical models are constructed for both problem specifications.
The block short-cut method can be used for complex columns and non-standard problem specifications.
Finally, some examples are presented to confirm that the methodology presented here can provide flexible structures satisfying the problem specifications.
In this paper we tackle the issue of the automatic recognition of functional dependencies among guessed predicates in constraint problem specifications.
The inference engine will drive the decision tree to explore the most probable option of numerical model and parameters matching the real problem specifications.
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