Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making proble...
Markov Decision Processes (MDPs) have been extensively studied and used in the context of planning and decision-making, and many methods exist to find the optimal policy for probl...
This paper addresses the problem of extending the lifetime of a batterypowered mobile host in a client-server wireless network by using task migration and remote processing. This ...
Markov Decision Processes (MDPs), currently a popular method for modeling and solving decision theoretic planning problems, are limited by the Markovian assumption: rewards and dy...
- This paper addresses the problem of maximizing capacity utilization of the battery power source in a portable electronic system under latency and loss rate constraints. First, a ...