TY - GEN
T1 - Learning Broadcast Protocols with LeoParDS
AU - Izsak, Noa
AU - Fisman, Dana
AU - Jacobs, Swen
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - LeoParDS is a new tool for learning broadcast protocols (BPs) from a set of positive and negative example traces. It is the first tool that enables learning of a distributed computational model in a parameterized setting, i.e., with a parametric number of processes running the BP concurrently. We describe the tool along a running example, discuss some implementation details, and present experimental results on randomly generated BPs.
AB - LeoParDS is a new tool for learning broadcast protocols (BPs) from a set of positive and negative example traces. It is the first tool that enables learning of a distributed computational model in a parameterized setting, i.e., with a parametric number of processes running the BP concurrently. We describe the tool along a running example, discuss some implementation details, and present experimental results on randomly generated BPs.
KW - broadcast protocols
KW - concurrent systems
KW - learning computational models
KW - parameterized verification
UR - https://www.scopus.com/pages/publications/85219204818
U2 - 10.1007/978-3-031-78709-6_11
DO - 10.1007/978-3-031-78709-6_11
M3 - Conference contribution
AN - SCOPUS:85219204818
SN - 9783031787089
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 220
EP - 234
BT - Automated Technology for Verification and Analysis - 22nd International Symposium, Proceedings
A2 - Akshay, S.
A2 - Niemetz, Aina
A2 - Sankaranarayanan, Sriram
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Symposium on Automated Technology for Verification and Analysis, ATVA 2024
Y2 - 21 October 2024 through 25 October 2024
ER -