This paper addresses the problem of scheduling jobs in soft real-time systems, where the utility of completing each job decreases over time. We present a utility-based framework fo...
In this paper we study the topic of CBR systems learning from observations in which those observations can be represented as stochastic policies. We describe a general framework wh...
Kellen Gillespie, Justin Karneeb, Stephen Lee-Urba...
Abstract— The study of protocol behavior and traffic characteristics in a simulated environment is commonly supported by ad-hoc or general purpose simulators (e.g., Opnet, NS-2)...
A new quality of service (QoS) aware disk scheduling algorithm is presented. It is applicable in environments where data requests arrive with different QoS requirements such as re...
Walid G. Aref, Khaled El-Bassyouni, Ibrahim Kamel,...
RVRL (Rule Value Reinforcement Learning) is a new algorithm which extends an existing learning framework that models the environment of a situated agent using a probabilistic rule...