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We can now formalize this structure using the tools of game theory.
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The work of Doignon and Falmagne in knowledge space theory (KST) represents one of the earliest and most rigorous attempts to formalize the structure of a knowledge domain for the purposes of adaptivity (Doignon and Falmagne 1985).
To formalize this transformation [20], the first step is that the structure, so called metamodel, of both UML and Petri Net models must be known.
We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches.
We formalize this result below.
We formalize this method next.
We formalize this definition as follows.
We can formalize this as follows.
The first contribution we make is to formalize this notion.
Reichenbach was the first to formalize this idea rather precisely.
We formalize this problem as follows [28, 89].
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