This paper surveys the field of reinforcement learning from a computer-science perspective.
It is written to be accessible to researchers familiar with machine learning. Both the historical …

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 …

Previous work on assigning tasks to robots has proposed extensive categorizations of
allocation of tasks with and without constraints. The main contribution of this paper is a …

There has been a growing interest in AI in the design of multiagent systems, especially in
multiagent cooperative planning. In this paper, we investigate the extent to which methods …

In this paper, we describe the partially observable Markov decision process (pomdp)
approach to nding optimal or near-optimal control strategies for partially observable …

Markov decision problems (MDPs) provide the foundations for a number of problems of
interest to AI researchers studying automated planning and reinforcement learning. In this …

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 …

Over the years increasingly sophisticated planning algorithms have been developed. These
have made for more efficient planners, but unfortunately these planners still suffer from …

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 …

Markov decision processes (MDPs) have recently been applied to the problem of modeling
decision-theoretic planning. While such traditional methods for solving MDPs are often …