1,044 research outputs found
Modelling and Simulation of Asynchronous Real-Time Systems using Timed Rebeca
In this paper we propose an extension of the Rebeca language that can be used
to model distributed and asynchronous systems with timing constraints. We
provide the formal semantics of the language using Structural Operational
Semantics, and show its expressiveness by means of examples. We developed a
tool for automated translation from timed Rebeca to the Erlang language, which
provides a first implementation of timed Rebeca. We can use the tool to set the
parameters of timed Rebeca models, which represent the environment and
component variables, and use McErlang to run multiple simulations for different
settings. Timed Rebeca restricts the modeller to a pure asynchronous
actor-based paradigm, where the structure of the model represents the service
oriented architecture, while the computational model matches the network
infrastructure. Simulation is shown to be an effective analysis support,
specially where model checking faces almost immediate state explosion in an
asynchronous setting.Comment: In Proceedings FOCLASA 2011, arXiv:1107.584
The earlier the better: a theory of timed actor interfaces
Programming embedded and cyber-physical systems requires attention not only to functional behavior and correctness, but also to non-functional aspects and specifically timing and performance. A structured, compositional, model-based approach based on stepwise refinement and abstraction techniques can support the development process, increase its quality and reduce development time through automation of synthesis, analysis or verification. Toward this, we introduce a theory of timed actors whose notion of refinement is based on the principle of worst-case design that permeates the world of performance-critical systems. This is in contrast with the classical behavioral and functional refinements based on restricting sets of behaviors. Our refinement allows time-deterministic abstractions to be made of time-non-deterministic systems, improving efficiency and reducing complexity of formal analysis. We show how our theory relates to, and can be used to reconcile existing time and performance models and their established theories
The earlier the better: a theory of timed actor interfaces
Programming embedded and cyber-physical systems requires attention not only to functional behavior and correctness, but also to non-functional aspects and specifically timing and performance constraints. A structured, compositional, model-based approach based on stepwise refinement and abstraction techniques can support the development process, increase its quality and reduce development time through automation of synthesis, analysis or verification. For this purpose, we introduce in this paper a general theory of timed actor interfaces. Our theory supports a notion of refinement that is based on the principle of worst-case design that permeates the world of performance-critical systems. This is in contrast with the classical behavioral and functional refinements based on restricting or enlarging sets of behaviors. An important feature of our refinement is that it allows time-deterministic abstractions to be made of time-non-deterministic systems, improving efficiency and reducing complexity of formal analysis. We also show how our theory relates to, and can be used to reconcile a number of existing time and performance models and how their established theories can be exploited to represent and analyze interface specifications and refinement steps.\u
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
We demonstrate a reinforcement learning agent which uses a compositional
recurrent neural network that takes as input an LTL formula and determines
satisfying actions. The input LTL formulas have never been seen before, yet the
network performs zero-shot generalization to satisfy them. This is a novel form
of multi-task learning for RL agents where agents learn from one diverse set of
tasks and generalize to a new set of diverse tasks. The formulation of the
network enables this capacity to generalize. We demonstrate this ability in two
domains. In a symbolic domain, the agent finds a sequence of letters that is
accepted. In a Minecraft-like environment, the agent finds a sequence of
actions that conform to the formula. While prior work could learn to execute
one formula reliably given examples of that formula, we demonstrate how to
encode all formulas reliably. This could form the basis of new multitask agents
that discover sub-tasks and execute them without any additional training, as
well as the agents which follow more complex linguistic commands. The
structures required for this generalization are specific to LTL formulas, which
opens up an interesting theoretical question: what structures are required in
neural networks for zero-shot generalization to different logics?Comment: Accepted in IROS 202
A Reo model of Software Defined Networks
Reo is a compositional coordination language for component connectors with a formal semantics based on automata. In this paper, we propose a formal model of software defined networks (SDNs) based on Reo where declarative constructs comprising of basic Reo primitives compose to specify descriptive models of both data and control planes of SDNs. We first describe the model of an SDN switch which can be compactly represented as a single state constraint automaton with a memory storing its flow table. A full network can then be compositionally constructed by composing the switches with basic communication channels. The reactive and proactive behaviour of the controllers in the control plane of an SDN can also be modelled by Reo connectors, which can compose the connectors representing data plane. The resulting model is suitable for testing, simulation, visualization, verification, and ultimately compilation into SDN switch code using the standard tools already available for Reo
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