Reinforcement learning covers a broad category of control problems in which the learning agent interacts with the environment in order to learn to maximize the collected utility. Such exploratory interaction is often costly, encouraging sample-efficient algorithms to be used in…

The construction of accurate predictive models over sequence data is of fundamental importance in the pursuit of developing artificial agents that are capable of (near)-optimal sequential decision-making in disparate environments.If such predictive models are available, they can be exploited by…

This thesis explores the challenges involved in building autonomous exploration and monitoring systems, and makes contributions on four fronts: describing the semantic context of the collected data, summarizing this information, deciding where to collect this data, and making optimal online…

In reasoning about multi-agent systems, it is important to look beyond the realm of propositional logic and to reason about the knowledge of agents within the system, as what they know about the environment will affect how they behave. A…

Semantic background knowledge is crucial for many intelligent applications. A classical way to represent such knowledge is through semantic networks. Wikipedia's hyperlink graph can be considered a primitive semantic network, since the links it contains usually correspond to semantic relationships…

Reinforcement Learning is a branch of Artificial Intelligence addressing the problem of single-agent autonomous sequential decision making. It proposes computational models which do not rely on the complete knowledge of the dynamics of stochastic environments. Options are a formalism used…

Learning agents that interact with complex environments often cannot predict the exact outcome of their actions due to noisy sensors or incomplete knowledge of the world. Learning the internal representation of such partially observable environments has proven to be a…