In this article, we show how psychological theories of emotion shed light on the interaction
between emotion and cognition, and thus can inform the design of human-like autonomous …

In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states
are those states entering which is undesirable or dangerous. We define the risk with respect …

In this paper we address the problem of automatically generating job workflows for the Grid.
These workflows describe the execution of a complex application built from individual …

Abstract Digital Twins (DTs) are considered key components in smart manufacturing. They
bridge the virtual and real world with the goal to model, understand, predict, and optimize …

As the capabilities of artificial intelligence (AI) systems improve, it becomes important to
constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of …

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 …

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 …

Controlled stochastic systems occur in science engineering, manufacturing, social sciences,
and many other cntexts. If the systems is modeled as a Markov decision process (MDP) and …

This paper provides a survey of motion planning techniques under uncertainty with a focus
on their application to autonomous guidance of unmanned aerial vehicles (UAVs). The …

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 …