We propose an online algorithm for planning under uncertainty in multi-agent settings modeled as DEC-POMDPs. The algorithm helps overcome the high computational complexity of solv...
We study the problem of on-line scheduling of parallel jobs on two machines. The jobs are parallel in the sense that each of them specifies the number of processors, in this case ...
Wun-Tat Chan, Francis Y. L. Chin, Deshi Ye, Guochu...
This paper addresses nonclairvoyant and nonpreemptive online job scheduling in Grids. In the applied basic model, the Grid system consists of a large number of identical processor...
Uwe Schwiegelshohn, Andrei Tchernykh, Ramin Yahyap...
Online target tracking requires to solve two problems: data association and online dynamic estimation. Usually, association effectiveness is based on prior information and observa...
We consider a non-preemptive, stochastic parallel machine scheduling model with the goal to minimize the weighted completion times of jobs. In contrast to the classical stochastic ...