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 modeling of continuous dynamics by logic programs composed of rules whose conditions and conclusions represent continuums of values. © Springer International Publishing AG, part of Springer Nature 2018.
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Tony Ribeiro, Sophie Tourret, Maxime Folschette, Morgan Magnin, Domenico Borzacchiello, et al.. Inductive learning from state transitions over continuous domains. ILP 2017 - 27th International Conference on Inductive Logic Programming, Sep 2017, Orléans, France. pp.124-139, ⟨10.1007/978-3-319-78090-0_9⟩. ⟨hal-01888952⟩



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