E-LIB Suche - Ergebnisse für: Muškardin, Edi

1

Active model learning of stochastic reactive systems (exten..:

Muškardin, Edi ; Tappler, Martin ; Aichernig, Bernhard K..
Software and Systems Modeling. 23 (2024) 2 - p. 503-524 , 2024

Link: https://doi.org/10.1007/..

2

Reinforcement Learning Under Partial Observability Guided b..:

, In: Lecture Notes in Computer Science; Integrated Formal Methods,

Muškardin, Edi ; Tappler, Martin ; Aichernig, Bernhard K.. - p. 257-276 , 2023

Link: https://doi.org/10.1007/..

3

Automata Learning Meets Shielding:

, In: Leveraging Applications of Formal Methods, Verification and Validation. Verification Principles; Lecture Notes in Computer Science,

Tappler, Martin ; Pranger, Stefan ; Könighofer, Bettina... - p. 335-359 , 2022

Link: https://doi.org/10.1007/..

4

Learning Finite State Models from Recurrent Neural Networks:

, In: Lecture Notes in Computer Science; Integrated Formal Methods,

Muškardin, Edi ; Aichernig, Bernhard K. ; Pill, Ingo. - p. 229-248 , 2022

Link: https://doi.org/10.1007/..

5

AALpy: an active automata learning library:

Muškardin, Edi ; Aichernig, Bernhard K. ; Pill, Ingo..
Innovations in Systems and Software Engineering. 18 (2022) 3 - p. 417-426 , 2022

Link: https://doi.org/10.1007/..

6

Active Model Learning of Stochastic Reactive Systems:

, In: Software Engineering and Formal Methods; Lecture Notes in Computer Science,

Tappler, Martin ; Muškardin, Edi ; Aichernig, Bernhard K.. - p. 481-500 , 2021

Link: https://doi.org/10.1007/..

7

AALpy: An Active Automata Learning Library:

, In: Automated Technology for Verification and Analysis; Lecture Notes in Computer Science,

Muškardin, Edi ; Aichernig, Bernhard K. ; Pill, Ingo.. - p. 67-73 , 2021

Link: https://doi.org/10.1007/..

8

CatIO - A Framework for Model-Based Diagnosis of Cyber-Phys..:

, In: Lecture Notes in Computer Science; Foundations of Intelligent Systems,

Muškardin, Edi ; Pill, Ingo ; Wotawa, Franz - p. 267-276 , 2020

Link: https://doi.org/10.1007/..

9

On the Relationship Between RNN Hidden State Vectors and Se..:

Muškardin, Edi ; Tappler, Martin ; Pill, Ingo..
http://arxiv.org/abs/2306.16854. , 2023

Link: http://arxiv.org/abs/230..

10

Learning Environment Models with Continuous Stochastic Dyna..:

Tappler, Martin ; Muškardin, Edi ; Aichernig, Bernhard K.
http://arxiv.org/abs/2306.17204. , 2023

Link: http://arxiv.org/abs/230..

11

Automata Learning meets Shielding:

Tappler, Martin ; Pranger, Stefan ; Könighofer, Bettina...
http://arxiv.org/abs/2212.01838. , 2022

Link: http://arxiv.org/abs/221..

12

Active vs. Passive: A Comparison of Automata Learning Parad..:

Aichernig, Bernhard K ; Muškardin, Edi ; Pferscher, Andrea
http://arxiv.org/abs/2209.14031. , 2022

Link: http://arxiv.org/abs/220..

13

Reinforcement Learning under Partial Observability Guided b..:

Muskardin, Edi ; Tappler, Martin ; Aichernig, Bernhard K.
http://arxiv.org/abs/2206.11708. , 2022

Link: http://arxiv.org/abs/220..

14

Learning-Based Fuzzing of IoT Message Brokers:

Aichernig, Bernhard K ; Muškardin, Edi ; Pferscher, Andrea
doi:10.5281/zenodo.4659890. , 2021

Link: https://zenodo.org/recor..

15

Learning-Based Fuzzing of IoT Message Brokers:

Aichernig, Bernhard K ; Muškardin, Edi ; Pferscher, Andrea
doi:10.5281/zenodo.4659890. , 2021

Link: https://zenodo.org/recor..