Learning Logic Program Representation for Delayed Systems With Limited Training Data

Abstract : Understanding the influences between components of dynamical systems such as biological networks, cellular automata or social networks provides insights to their dynamics. Influences of such dynamical systems can be represented by logic programs with delays. Logical methods that learn logic programs from observations have been developed, but their practical use is limited since they cannot handle noisy input and need a huge amount of data to give accurate results. In this paper , we present a method that learns to distinguish different dynamical systems with delays based on Recurrent Neural Network (RNN). This method relies on Long Short-Term Memory (LSTM) to extract and encode features from input sequences of time series data. We show that the produced high dimensional encoding can be used to distinguish different dynamical systems and reproduce their specific behaviors.
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Contributor : Tony Ribeiro <>
Submitted on : Friday, April 13, 2018 - 1:56:40 PM
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  • HAL Id : hal-01766236, version 1



Yin Phua, Tony Ribeiro, Sophie Tourret, Katsumi Inoue. Learning Logic Program Representation for Delayed Systems With Limited Training Data. the 27th International Conference on Inductive Logic Programming, Sep 2017, Orléans, France. ⟨hal-01766236⟩



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