This paper explains how Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken di...
Steve Young, Milica Gasic, Simon Keizer, Fran&cced...
Partially Observable Markov Decision Process models (POMDPs) have been applied to low-level robot control. We show how to use POMDPs differently, namely for sensorplanning in the ...
Interval availability is a dependability measure defined by the fraction of time during which a system is in operation over a finite observation period. The computation of its d...
The traditional model transformation approach is to write transformation programs in a specialized language. Although such languages provide powerful capabilities to automate mode...
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this ...