Outline Objective Background: Stochastic tools used in healthcare MDP in healthcare Preliminaries Optimality Equations and the Principle of Optimality Solving MDPs Examples References Objective: To discuss the construction and evaluation of Markov Decision Process (MDP) To investigate the role of MDP in healthcare. To identify the most appropriate solution techniques for finite and infinite-hor...

In this paper, we considered decision processes in which one decision is made in each process. We incorporate the utility function concept into the decision process, derived the utility function in fuzzy events and by the max-product operation obtained the utility possibility measure of the fuzzy events. In cases with numerous decision processes, the optimum action can be determined from the re...

This paper presents the Markov decision process extraction network, which is a data-efficient, automatic state estimation approach for discrete-time reinforcement learning (RL) based on recurrent neural networks. The architecture is designed to model the minimal relevant dynamics of an environment, capable of condensing large sets of continuous observables to a compact state representation and ...

In this paper, we deal with a multicriteria competitive Markov decision process. In the decision process there are two decision makers with a competitive behaviour, so they are usually called players. Their rewards are coupled because depend on the actions chosen by both players in each state of the process. We propose as solution of this game the set of Pareto-optimal security strategies for a...

Markov decision process (MDP) provides the foundations for a number of problems, such as artificial intelligence studying, automated planning and reinforcement learning. MDP can be solved efficiently in theory. However, for large scenarios, more investigations are needed to reveal practical algorithms. Algorithms for solving MDP have a natural concurrency. In this paper, we present parallel alg...

Discrete time Markov chains with multidimensional state space are considered where the coordinates are locally interacting and develop synchronously. The interaction structure of the process is given by some general graph. Decision makers control the sys-tem's behaviour on the coordinate level using only local information. In the class of local strategies there exist deterministic stationary st...

A batch production process that is initially in the in-control state can fail with constant failure rate to the out-of-control state. The probability that a unit is conforming if produced while the process is in control is constant and higher than the respective constant conformance probability while the process is out of control. When production ends, the units are inspected in the order they ...

Social choice theory provides insights into a variety of collective decision making settings, but nowadays some of its tenets are challenged by Internet environments, which call for dynamic decision making under constantly changing preferences. In this paper we model the problem via Markov decision processes (MDP), where the states of the MDP coincide with preference profiles and a (determinist...

In this paper we analyse a queueing network consisting of parallel queues and arriving customers which have to be assigned to one of the queues. The assignment rule may not depend on the numbers of customers in the queues. Our goal is to find a policy which is optimal with respect to the long run average cost. We will consider two cases, holding costs and waiting times. A recently developed alg...