We address performance issues associated with simulationbased algorithms for optimizing Markov reward processes. Specifically, we are concerned with algorithms that exploit the re...
Researchers often express probabilistic planning problems as Markov decision process models and then maximize the expected total reward. However, it is often rational to maximize ...
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...
Recently, a parametric State Reward Markov Model SRMM p has been developed for the reliability and availability analysis of self-healing SONET mesh networks 2 . In this paper, w...
Abstract. This paper presents parallel approaches to the complete transient numerical analysis of stochastic reward nets (SRNs) for both shared and distributed-memory machines. Par...