Automated planning, the problem of how an agent achieves a goal given a repertoire of
actions, is one of the foundational and most widely studied problems in the AI literature. The …

The study of new medical treatments, and sequences of treatments, is inextricably linked
with statistics. Without statistical estimation and inference, we are left with case studies and …

Recent advancements in reinforcement learning confirm that reinforcement learning
techniques can solve large scale problems leading to high quality autonomous decision …

The paper presents the author's partial and personal historical reconstruction of how
decision theory is evolving to a decision aiding methodology. The presentation shows …

We desribe a variation of the planning domain definition language, PDDL, that permits the
modeling of probabilistic planning problems with rewards. This language, PPDDL1. 0, was …

In the last few years, there has been increasing interest from the agent community in the use
of techniques from decision theory and game theory. Our aims in this article are firstly to …

The ultimate objective in planning is to construct plans for execution. However, when a plan
is executed in a real environment it can encounter differences between the expected and …

We introduce a unified framework that interrelates three different types of policies that will be
used in autonomic computing system: action, goal, and utility function policies. Our policy …

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and
algorithmic strategy for solving problems that are sometimes large and complex, and are …

Nowadays, the complex manufacturing processes have to be dynamically modelled and
controlled to optimise the diagnosis and the maintenance policies. This article presents a …