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Journal Articles Machine Learning Year : 2021

Learning any memory-less discrete semantics for dynamical systems represented by logic programs

Tony Ribeiro
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Maxime Folschette
Morgan Magnin
Katsumi Inoue
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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 handled were mainly restricted to synchronous deterministic dynamics. However, other dynamics exist in the field of logical modeling, in particular the asynchronous semantics which is widely used to model biological systems. In this paper, we propose a modeling of discrete memory-less multi-valued dynamic systems as logic programs in which a rule represents what can occur rather than what will occur. This modeling allows us to represent non-determinism and to propose an extension of LFIT to learn regardless of the update schemes, allowing to capture a large range of semantics. We also propose a second algorithm which is able to learn a whole system dynamics, including its semantics, in the form of a single propositional logic program with constraints. We show through theoretical results the correctness of our approaches. Practical evaluation is performed on benchmarks from biological literature.
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Dates and versions

hal-02925942 , version 1 (31-08-2020)
hal-02925942 , version 2 (02-09-2020)
hal-02925942 , version 3 (22-06-2021)
hal-02925942 , version 4 (26-06-2021)
hal-02925942 , version 5 (13-10-2021)

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Tony Ribeiro, Maxime Folschette, Morgan Magnin, Katsumi Inoue. Learning any memory-less discrete semantics for dynamical systems represented by logic programs. Machine Learning, 2021, ⟨10.1007/s10994-021-06105-4⟩. ⟨hal-02925942v5⟩
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