—Reinforcement learning is a framework in which an agent can learn behavior without knowledge on a task or an environment by exploration and exploitation. Striking a balance betw...
Zhengqiao Ji, Q. M. Jonathan Wu, Maher A. Sid-Ahme...
Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle t...
Ruibin Bai, Edmund K. Burke, Graham Kendall, Jingp...
Load balancing is a key concern when developing parallel and distributed computing applications. The emergence of computational grids extends this problem, where issues of cross-d...
Junwei Cao, Daniel P. Spooner, Stephen A. Jarvis, ...
In the context of computer-assisted plant identification we are facing challenging information retrieval problems because of the very high within-class variability and of the lim...
In this paper, we present a preemptive joint scheduling of hard deadline periodic and hard deadline aperiodic tasks on a uniprocessor real-time system. The scheduling has extended...