Most existing semi-supervised learning methods are based on the smoothness assumption that data points in the same high density region should have the same label. This assumption, ...
Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that gr...
Constraint Programming is an attractive approach for solving AI planning problems by modelling them as Constraint Satisfaction Problems (CSPs). However, formulating effective cons...
Andrea Rendl, Ian Miguel, Ian P. Gent, Peter Grego...
When describing the resource usage of a program, it is usual to talk in asymptotic terms, such as the well-known “big O” notation, whereby we focus on the behaviour of the prog...
Elvira Albert, Diego Alonso, Puri Arenas, Samir Ge...
Annals and chronicles may be the foundation of accounting, but writers of stories and histories have long known that they seldom render a satisfactory account of complex events. I...