The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Dynamic programming provides a methodology to develop planners and controllers for nonlinear systems. However, general dynamic programming is computationally intractable. We have ...
Understanding large software systems is difficult. Traditionally, automated tools are used to assist program understanding. However, the representations constructed by these tool...
The elastic task model proposed by Buttazzo, et. al. [9] is a powerful model for adapting real-time systems in the presence of uncertainty. This paper generalizes the existing ela...
Thidapat Chantem, Xiaobo Sharon Hu, Michael D. Lem...
This paper studies a version of the job shop scheduling problem in which some operations have to be scheduled within non-relaxable time windows i.e. earliest latest possible start...