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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