Decision-theoretic systems, such as Markov Decision Processes (MDPs), are used for
sequential decision-making under uncertainty. MDPs provide a generic framework that can …

Markov decision processes (MDPs) have recently been proposed as useful conceptual
models for understanding decision-theoretic planning. However, the utility of the associated …

This paper addresses the problem of planning under uncertainty in large Markov Decision
Processes (MDPs). Factored MDPs represent a complex state space using state variables …

For many applications of Markov Decision Processes (MDPs), the transition function cannot
be specified exactly. Bayes-Adaptive MDPs (BAMDPs) extend MDPs to consider transition …

Planning under uncertainty is a central problem in the study of automated sequential
decision making, and has been addressed by researchers in many different fields, including …

Markov decision processes (MDPs) have proven to be popular models for decision-theoretic
planning, but standard dynamic programming algorithms for solving MDPs rely on explicit …

Abstract Models for sequential decision making under uncertainty (eg, Markov decision
processes, or MDPs) have been studied in operations research for decades. The recent …

Many real-world problems fall in the category of sequential decision-making under
uncertainty; Markov Decision Processes (MDPs) are a common method for modeling such …

The goal of this chapter is to provide an introduction to Markov decision processes as a
framework for sequential decision making under uncertainty. The aim of this introduction is …

Many large MDPs can be represented compactly using a dynamic Bayesian network.
Although the structure of the value function does not retain the structure of the process …