In sequential decision-making problems formulated as Markov decision processes, state-value function approximation using domain features is a critical technique for scaling up the...
The precise specification of reward functions for Markov decision processes (MDPs) is often extremely difficult, motivating research into both reward elicitation and the robust so...
A method of rare event simulation, termed here quantum simulation, and known also (with some variations) as population Monte Carlo, and Sequential Markov Chain simulation, is appli...
Abstract—We propose a strategy to perform query processing on P2P similarity search systems based on peers and superpeers. We show that by approximating global but resumed inform...
Moving object detection is essential for real-time surveillance; however, it is challenging to support moving object detection in a timely fashion due to the compute-intensive natu...