Algorithms for evolutionary computation, which simulate the process of natural selection to
solve optimization problems, are an effective tool for discovering high-performing …

In this chapter, we present the multistage stochastic programming framework for sequential
decision making under uncertainty. We discuss its differences with Markov Decision …

Privacy policies often place restrictions on the purposes for which a governed entity may use
personal information. For example, regulations, such as the Health Insurance Portability and …

Lifted inference approaches have rendered large, previously intractable probabilistic
inference problems quickly solvable by handling whole sets of indistinguishable objects …

In this dissertation we study strategy iteration (also known as policy iteration) algorithms for
solving Markov decision processes (MDPs) and two-player turn-based stochastic games …

Markov decision processes (MDPs) provide an expressive framework for planning in
stochastic domains. However, exactly solving a large MDP is often intractable due to the …

The ability to learn a policy for a sequential decision problem with continuous state space
using on-line data is a long-standing challenge. This paper presents a new reinforcement …

Intervention in gene regulatory networks in the context of Markov decision processes has
usually involved finding an optimal one-transition policy, where a decision is made at every …

Collision avoidance is the essential requirement for unmanned aerial vehicles (UAVs) to
become fully autonomous. Several algorithms have been proposed to do the path planning …

Motion planning for planetary rovers must consider control uncertainty in order to maintain
the safety of the platform during navigation. Modelling such control uncertainty is difficult due …