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 …

The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents
interact with each other. These agents are considered to be autonomous entities such as …

In this paper, we bring techniques from operations research to bear on the problem of
choosing optimal actions in partially observable stochastic domains. We begin by …

Decision is obviously related to reasoning. One of the possible definitions of artificial
intelligence (AI) refers to cognitive processes and especially to reasoning. Before making …

In planning, goals have traditionally been viewed as specifying a set of desirable final states.
Any plan that transforms the current state to one of these desirable states is viewed to be …

The recent advances in computer speed and algorithms for probabilistic inference have led
to a resurgence of work on planning under uncertainty. The aim is to design AI planners for …

We define the probabilistic planning problem in terms of a probability distribution over initial
world states, a boolean combination of propositions representing the goal, a probability …

Planning research in Artificial Intelligence (AI) has often focused on problems where there
are cascading levels of action choice and complex interactions between actions. In contrast …

This article provides an overview of the field of qualitative decision theory: its motivating
tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies …

Abstract Partially Observable Markov Decision Process (POMDP) is a general sequential
decision-making model where the effects of actions are nondeterministic and only partial …