no code implementations • 19 Feb 2024 • Mojtaba Valizadeh, Nathanaël Fijalkow, Martin Berger
Linear temporal logic (LTL) is widely used in industrial verification.
no code implementations • 18 Feb 2024 • Guruprerana Shabadi, Nathanaël Fijalkow, Théo Matricon
The field of Reinforcement Learning (RL) is concerned with algorithms for learning optimal policies in unknown stochastic environments.
no code implementations • 26 Dec 2023 • Corto Mascle, Nathanaël Fijalkow, Guillaume Lagarde
We study the problem of learning linear temporal logic (LTL) formulas from examples, as a first step towards expressing a property separating positive and negative instances in a way that is comprehensible for humans.
no code implementations • 15 Mar 2023 • Théo Matricon, Nathanaël Fijalkow, Gaëtan Margueritte
WikiCoder solves tasks that no program synthesizers were able to solve before thanks to the use of knowledge graphs, while integrating with recent developments in the field to operate at scale.
1 code implementation • 24 Oct 2021 • Nathanaël Fijalkow, Guillaume Lagarde, Théo Matricon, Kevin Ellis, Pierre Ohlmann, Akarsh Potta
We investigate how to augment probabilistic and neural program synthesis methods with new search algorithms, proposing a framework called distribution-based search.
1 code implementation • 13 Oct 2021 • Ritam Raha, Rajarshi Roy, Nathanaël Fijalkow, Daniel Neider
Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas.
no code implementations • 1 Feb 2021 • Nathanaël Fijalkow, Guillaume Lagarde
In this paper we initiate the study of the computational complexity of learning linear temporal logic (LTL) formulas from examples.
no code implementations • 25 Nov 2020 • Antonio Casares, Thomas Colcombet, Nathanaël Fijalkow
In this paper, we are interested in automata over infinite words and infinite duration games, that we view as general transition systems.
Formal Languages and Automata Theory F.4.3
1 code implementation • 6 Nov 2019 • Judith Clymo, Haik Manukian, Nathanaël Fijalkow, Adrià Gascón, Brooks Paige
A particular challenge lies in generating meaningful sets of inputs and outputs, which well-characterize a given program and accurately demonstrate its behavior.
2 code implementations • 11 Oct 2019 • Nathanaël Fijalkow, Mohit Kumar Gupta
The success of neural networks across most machine learning tasks and the persistence of adversarial examples have made the verification of such models an important quest.