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Inductive Learning from State Transitions over Continuous Domains


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.
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hal-01655644 , version 1 (05-12-2017)
hal-01655644 , version 2 (08-01-2020)
hal-01655644 , version 3 (08-01-2020)



Tony Ribeiro, Sophie Tourret, Maxime Folschette, Morgan Magnin, Domenico Borzacchiello, et al.. Inductive Learning from State Transitions over Continuous Domains. 27th International Conference on Inductive Logic Programming, LNCS, volume 10759, Springer, Cham, pp.124-139, 2018, Inductive Logic Programming, ⟨10.1007/978-3-319-78090-0_9⟩. ⟨hal-01655644v3⟩
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