Developing autonomous systems that operate successfully in dynamic environments entails
many challenges. Researchers introduce the concept of adjustable autonomy to mitigate …

Software agent technology has been intensively explored in the past three decades. It is
explicitly or implicitly applied in many systems. Software agent research covers a wide range …

The goal of a traditional Markov decision process (MDP) is to maximize expected cumulative
reward over a defined horizon (possibly infinite). In many applications, however, a decision …

Abstract While Markov Decision Processes (MDPs) have been shown to be effective models
for planning under uncertainty, the objective to minimize the expected cumulative cost is …

In this article, we consider shortest path problems in a directed graph where the transitions
between nodes are subject to uncertainty. We use a minimax formulation, where the …

In piecewise-deterministic Markov processes (PDMPs) the state of a finite-dimensional
system evolves continuously, but the evolutive equation may change randomly as a result of …

In the Markov decision process model, policies are usually evaluated by expected
cumulative rewards. As this decision criterion is not always suitable, we propose in this …

We tackle the problem of finding optimal policies for Markov Decision Processes, that
minimize the probability of the cumulative cost exceeding a given budget. Such task falls …

In this paper, we investigate the control of a cyber–physical system (CPS) while accounting
for its vulnerability to external attacks. We formulate a constrained stochastic problem with a …

Classical deterministic optimal control problems assume full information about the controlled
process. The theory of control for general partially-observable processes is powerful, but the …