Learning any memory-less discrete semantics for dynamical systems represented by logic programs
From MaRDI portal
Publication:2102410
DOI10.1007/s10994-021-06105-4OpenAlexW3216651657MaRDI QIDQ2102410
Katsumi Inoue, Tony Ribeiro, Morgan Magnin, Maxime Folschette
Publication date: 28 November 2022
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-021-06105-4
Related Items (2)
Differentiable learning of matricized DNFs and its application to Boolean networks ⋮ Condition for periodic attractor in 4-dimensional repressilators
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- ILP turns 20. Biography and future challenges
- Incremental learning of event definitions with inductive logic programming
- Nonmonotonic abductive inductive learning
- Paraconsistent logic programming
- Dynamical behaviour of biological regulatory networks. II: Immunity control in bacteriophage lambda
- On learning gene regulatory networks under the Boolean network model
- Ultra-strong machine learning: comprehensibility of programs learned with ILP
- Best-effort inductive logic programming via fine-grained cost-based hypothesis generation. The Inspire system at the inductive logic programming competition
- Identification of biological transition systems using meta-interpreted logic programs
- Boolean networks: beyond generalized asynchronicity
- Identification of genetic networks by strategic gene disruptions and gene overexpressions under a Boolean model.
- A model for restriction point control of the mammalian cell cycle
- Inductive learning from state transitions over continuous domains
- Making sense of sensory input
- Learning from interpretation transition
- Oscillating Behavior of Logic Programs
- Non-atomic Transition Firing in Contextual Nets
- Quantitative deduction and its fixpoint theory
- Bilattices and the semantics of logic programming
- The Semantics of Predicate Logic as a Programming Language
- Iterative Learning of Answer Set Programs from Context Dependent Examples
- Learning Dynamics with Synchronous, Asynchronous and General Semantics
- Synchronism versus asynchronism in monotonic Boolean automata networks
- Concurrency in Boolean networks
This page was built for publication: Learning any memory-less discrete semantics for dynamical systems represented by logic programs