The problem of planning and goal-directed behavior has been addressed in computer
science for many years, typically based on classical concepts like Bellman's optimality …

In this paper, we review the role of probabilistic graphical models in artificial intelligence. We
start by giving an account of the early years when there was important controversy about the …

Developed from the authors' longstanding course on decision and risk analysis, Value-
Added Decision Making for Managers explores the important interaction between decisions …

Variational inference algorithms such as belief propagation have had tremendous impact on
our ability to learn and use graphical models, and give many insights for developing or …

Influence diagrams have become a popular tool for representing and solving complex
decision-making problems under uncertainty. In this paper, we focus on the task of building …

This paper provides a survey on probabilistic decision graphs for modeling and solving
decision problems under uncertainty. We give an introduction to influence diagrams, which …

While decision trees are a popular formal and quantitative method for determining an
optimal decision from a finite set of choices, for all but very simple problems they are …

Probabilistic graphical models such as Markov random fields, Bayesian networks and
decision networks (aka influence diagrams) provide powerful frameworks for representing …

As unmanned aerial vehicles (UAVs) are used more and more in military operations,
increasing their level of autonomous decision making becomes necessary. In uncertain …

Most optimisation research focuses on relatively simple cases: one decision maker, one
objective, and possibly a set of constraints. However, real-world optimisation problems often …