Automatic induction of DEVS structures
Machine Learning methods seem to help for model-building in the field of Systems Theory. In this work, we present a study on a method for automatically inducing a discrete event structure (DEVS) from descriptions of behaviours of a system. To this end, both inductive learning and DEVS formalisms have been made compatible in order to translate input data into a form usable by the inductor. Morover, the language used in classical inductive learning algorithms must be enhanced to cope with the temporal characteristics of input data.
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