We present a probabilistic model to monitor a user's emotions and engagement during the interaction with educational games. We illustrate how our probabilistic model assesses...
Recently there has been significant interest in employing probabilistic techniques for fault localization. Using dynamic dependence information for multiple passing runs, learnin...
Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it. Mathematical models fo...
Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
Abstract. Discovering the topology of a set of labeled data in a Euclidian space can help to design better decision systems. In this work, we propose a supervised generative model ...
This paper presents a novel framework for simultaneously learning representation and control in continuous Markov decision processes. Our approach builds on the framework of proto...