Formal modeling and analysis of timed systems : 20th International Conference, FORMATS 2022, Warsaw, Poland, September 13-15, 2022, Proceedings / Sergiy Bogomolov, David Parker (eds.).
FORMATS (Conference) (20th : 2022 : Warsaw, Poland); Bogomolov, Sergiy, editor.; Parker, David, 1977- editor.
2022
QA76.9.F67
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Title Formal modeling and analysis of timed systems : 20th International Conference, FORMATS 2022, Warsaw, Poland, September 13-15, 2022, Proceedings / Sergiy Bogomolov, David Parker (eds.).
Meeting Name FORMATS (Conference) (20th : 2022 : Warsaw, Poland)
ISBN 9783031158391 (electronic bk.)
3031158393 (electronic bk.)
9783031158384 (print)
DOI https://doi.org/10.1007/978-3-031-15839-1
Published Cham, Switzerland : Springer, 2022.
Language English
Description 1 online resource (xvii, 307 pages) : illustrations (some color).
Item Number 10.1007/978-3-031-15839-1 doi
Call Number QA76.9.F67
Dewey Decimal Classification 004.01/51
Summary This book constitutes the refereed proceedings of the 20th International Conference on Formal Modeling and Analysis of Timed Systems, FORMATS 2022, held in Warsaw, Poland, in September 2022. The 12 full papers together with 2 short papers that were carefully reviewed and selected from 30 submissions are presented in this volume with 3 full-length papers associated with invited/anniversary talks. The papers focus on topics such as modelling, design and analysis of timed computational systems. The conference aims in real-time issues in hardware design, performance analysis, real-time software, scheduling, semantics and verification of real-timed, hybrid and probabilistic systems.
Note Includes author index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 30, 2022).
Added Author Bogomolov, Sergiy, editor.
Parker, David, 1977- editor.
Series Lecture notes in computer science ; 13465. 1611-3349
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Invited Papers
Probabilistic and Timed Systems
Temporal Logic
Timed Automata and Games
Neural Networks
Reinforcement Learning.

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