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…

Reference documentation is an important source of information on API usage. Programmers, however, can easily overlook reference information because of its tedious nature, and because the information they seek can be buried among irrelevant or boiler-plate text. We propose to…

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…

Positron emission tomography (PET) imaging is becoming increasingly popular for understanding brain function. This thesis addresses two problems related to PET images: binding potential (BP) computation and pairwise PET image registration. We first investigate the influence of several computational choices…

Modern information systems, consisting of an application server tier and a database tier, offer several consistency guarantees for accessing data where strong consistency is traded for better performance or higher availability. However, it is often not clear how an application…