We address the problem of computing an optimal value function for Markov decision processes. Since finding this function quickly and accurately requires substantial computation ef...
The vast size of real world stochastic programming instances requires sampling to make them practically solvable. In this paper we extend the understanding of how sampling affects ...
We present an approximation method that solves a class of Decentralized hybrid Markov Decision Processes (DEC-HMDPs). These DEC-HMDPs have both discrete and continuous state variab...
Penetration Testing is a methodology for assessing network security, by generating and executing possible hacking attacks. Doing so automatically allows for regular and systematic...
Many stochastic planning problems can be represented using Markov Decision Processes (MDPs). A difficulty with using these MDP representations is that the common algorithms for so...