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

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...

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Bibliographic Details
Published in:Machine learning Vol. 111; no. 10; pp. 3593 - 3670
Main Authors: Ribeiro, Tony, Folschette, Maxime, Magnin, Morgan, Inoue, Katsumi
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2022
Springer Nature B.V
Springer Verlag
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Control
Dynamical systems
Learning
Logic
Logic in Computer Science
Logic programming
Logic programs
Machine Learning
Mechatronics
Modelling
Natural Language Processing (NLP)
Robotics
Semantics
Simulation and Modeling
Special issue on Learning and Reasoning
System dynamics
Logical modeling
Dynamic semantics
Inductive logic programming
Dynamic systems
ISSN:0885-6125, 1573-0565
Online Access:Get full text
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Summary: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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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-021-06105-4