23,074 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
A new model for solution of complex distributed constrained problems
In this paper we describe an original computational model for solving
different types of Distributed Constraint Satisfaction Problems (DCSP). The
proposed model is called Controller-Agents for Constraints Solving (CACS). This
model is intended to be used which is an emerged field from the integration
between two paradigms of different nature: Multi-Agent Systems (MAS) and the
Constraint Satisfaction Problem paradigm (CSP) where all constraints are
treated in central manner as a black-box. This model allows grouping
constraints to form a subset that will be treated together as a local problem
inside the controller. Using this model allows also handling non-binary
constraints easily and directly so that no translating of constraints into
binary ones is needed. This paper presents the implementation outlines of a
prototype of DCSP solver, its usage methodology and overview of the CACS
application for timetabling problems
Virtual to Real Reinforcement Learning for Autonomous Driving
Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real environment. In this paper, we propose a novel
realistic translation network to make model trained in virtual environment be
workable in real world. The proposed network can convert non-realistic virtual
image input into a realistic one with similar scene structure. Given realistic
frames as input, driving policy trained by reinforcement learning can nicely
adapt to real world driving. Experiments show that our proposed virtual to real
(VR) reinforcement learning (RL) works pretty well. To our knowledge, this is
the first successful case of driving policy trained by reinforcement learning
that can adapt to real world driving data
Research accomplished at the Knowledge Based Systems Lab: IDEF3, version 1.0
An overview is presented of the foundations and content of the evolving IDEF3 process flow and object state description capture method. This method is currently in beta test. Ongoing efforts in the formulation of formal semantics models for descriptions captured in the outlined form and in the actual application of this method can be expected to cause an evolution in the method language. A language is described for the representation of process and object state centered system description. IDEF3 is a scenario driven process flow modeling methodology created specifically for these types of descriptive activities
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