We present a technique for computing approximately optimal solutions to stochastic resource allocation problems modeled as Markov decision processes (MDPs). We exploit two key pro...
Nicolas Meuleau, Milos Hauskrecht, Kee-Eung Kim, L...
Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in whic...
Typical methods in CRM marketing include action selection on the basis of Markov Decision Processes with fixed transition probabilities on the one hand, and scoring customers separ...
We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements ...
This paper provides a technique, based on partially observable Markov decision processes (POMDPs), for building automatic recovery controllers to guide distributed system recovery...
Kaustubh R. Joshi, William H. Sanders, Matti A. Hi...