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.
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Notes
- 1.
Experiments sources: http://tonyribeiro.fr/data/experiments/ILP_2017.zip.
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Ribeiro, T. et al. (2018). Inductive Learning from State Transitions over Continuous Domains. In: Lachiche, N., Vrain, C. (eds) Inductive Logic Programming. ILP 2017. Lecture Notes in Computer Science(), vol 10759. Springer, Cham. https://doi.org/10.1007/978-3-319-78090-0_9
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