We present a novel approach for the automatic generation of model-to-model transformations given a description of the operational semantics of the source language in the form of gr...
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
An important requirement of model transformations is the preservation of the behavior of the original model. A model transformation is semantically correct if for each simulation r...
In this paper we describe an application of the theory of graph transformations to the practise of language design. In particular, we have defined the static and dynamic semantics ...
In this paper, we present a computational method for transforming a syntactic graph, which represents all syntactic interpretations of a sentence, into a semantic graph which filt...