We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long run...
Learning undirected graphical models such as Markov random fields is an important machine learning task with applications in many domains. Since it is usually intractable to learn...
Arthur Asuncion, Qiang Liu, Alexander T. Ihler, Pa...
The Gaussian kernel density estimator is known to have substantial problems for bounded random variables with high density at the boundaries. For i.i.d. data several solutions hav...
This paper presents the language Lutin and its operational semantics. This language specifically targets the domain of reactive systems, where an execution is a (virtually) infini...