Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
A new model of nonuniform traffic is introduced for a single-hop packet-switching system. This traffic model allows arbitrary traffic streams subject only to a constraint on the nu...
Abstract--In the Relational Reinforcement learning framework, we propose an algorithm that learns an action model allowing to predict the resulting state of each action in any give...
Scenarios and goals are effective and popular techniques for requirements definition. Validation is essential in order to ensure that they represent what stakeholders actually wan...
This paper studies the problem of model-based testing of real-time systems that are only partially observable. We model the System Under Test (SUT) using Timed Game Automata (TGA)...
Alexandre David, Kim Guldstrand Larsen, Shuhao Li,...