Traditional design techniques for embedded systems apply transformations on the source code to optimize hardwarerelated cost factors. Unfortunately, such transformations cannot ad...
Marijn Temmerman, Edgar G. Daylight, Francky Catth...
The goal of Reinforcement learning (RL) is to maximize reward (minimize cost) in a Markov decision process (MDP) without knowing the underlying model a priori. RL algorithms tend ...
Both the logic and the stochastic analysis of discrete-state systems are hindered by the combinatorial growth of the state space underlying a high-level model. In this work, we con...
Abstract. Business process models play an important role for the management, design, and improvement of process organizations and processaware information systems. Despite the exte...
Jan Mendling, Gustaf Neumann, Wil M. P. van der Aa...
Domain-specific modeling has become a popular way of designing and developing systems. It generally involves a systematic use of a set of object-oriented models to represent vari...