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Inductive Learning from State Transitions over Continuous Domains
Abstract : Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far, the systems that LFIT handles are restricted to discrete variables or suppose a discretization of continuous data. However, when working with real data, the discretization choices are critical for the quality of the model learned by LFIT. In this paper, we focus on a method that learns the dynamics of the system directly from continuous time-series data. For this purpose, we propose a modelling of continuous dynamics by logic programs composed of rules whose conditions and conclusions represent continuums of values.
Cited literature [15 references]
https://hal.archives-ouvertes.fr/hal-01655644
Contributor : Maxime Folschette Connect in order to contact the contributor
Submitted on : Wednesday, January 8, 2020 - 4:02:33 PM
Last modification on : Wednesday, April 27, 2022 - 3:48:00 AM
Contributor : Maxime Folschette Connect in order to contact the contributor
Submitted on : Wednesday, January 8, 2020 - 4:02:33 PM
Last modification on : Wednesday, April 27, 2022 - 3:48:00 AM
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