Información de la conferencia
FORMATS 2022: International Conference on Formal Modeling and Analysis of Timed Systems
https://conferences.ncl.ac.uk/formats2022/
Día de Entrega:
2022-05-04 Extended
Fecha de Notificación:
2022-06-17
Fecha de Conferencia:
2022-09-12
Ubicación:
Warsaw, Poland
Años:
20
QUALIS: b1   Vistas: 15618   Seguidores: 3   Asistentes: 1

Solicitud de Artículos
SCOPE & TOPICS

FORMATS (International Conference on Formal Modeling and Analysis of Timed Systems) is an annual conference which aims to promote the study of fundamental and practical aspects of timed systems, and to bring together researchers from different disciplines that share interests in the modelling, design and analysis of timed computational systems. The conference aims to attract researchers interested in real-time issues in hardware design, performance analysis, real-time software, scheduling, semantics and verification of real-timed, hybrid and probabilistic systems.

Typical topics include (but are not limited to):

    Foundations and Semantics: Theoretical foundations of timed systems, languages and models (e.g., timed automata, timed Petri nets, hybrid automata, timed process algebra, max-plus algebra, probabilistic models).

    Methods and Tools: Techniques, algorithms, data structures, and software tools for analyzing timed systems and resolving temporal constraints (e.g., scheduling, worst-case execution time analysis,optimization, model checking, testing, constraint solving).

    Applications: Adaptation and specialization of timing technology in application domains in which timing plays an important role (e.g., real-time software, hardware circuits, scheduling in manufacturing and telecommunication, robotics).

New for this year, FORMATS will incorporate a special track on:

    Learning-based and data-driven systems: We particularly encourage papers that exploit synergies between the formal analysis of timed systems and data-driven techniques (such as reinforcement learning or deep learning), or which target application domains where learning is important (such as robotics or autonomous systems).
Última Actualización Por Dou Sun en 2022-04-30
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