Sebastian Wrede


An Interaction-Centric Dataset for Learning Automation Rules in Smart Homes
Kai Frederic Engelmann | Patrick Holthaus | Britta Wrede | Sebastian Wrede
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The term smart home refers to a living environment that by its connected sensors and actuators is capable of providing intelligent and contextualised support to its user. This may result in automated behaviors that blends into the user’s daily life. However, currently most smart homes do not provide such intelligent support. A first step towards such intelligent capabilities lies in learning automation rules by observing the user’s behavior. We present a new type of corpus for learning such rules from user behavior as observed from the events in a smart homes sensor and actuator network. The data contains information about intended tasks by the users and synchronized events from this network. It is derived from interactions of 59 users with the smart home in order to solve five tasks. The corpus contains recordings of more than 40 different types of data streams and has been segmented and pre-processed to increase signal quality. Overall, the data shows a high noise level on specific data types that can be filtered out by a simple smoothing approach. The resulting data provides insights into event patterns resulting from task specific user behavior and thus constitutes a basis for machine learning approaches to learn automation rules.

How to Address Smart Homes with a Social Robot? A Multi-modal Corpus of User Interactions with an Intelligent Environment
Patrick Holthaus | Christian Leichsenring | Jasmin Bernotat | Viktor Richter | Marian Pohling | Birte Carlmeyer | Norman Köster | Sebastian Meyer zu Borgsen | René Zorn | Birte Schiffhauer | Kai Frederic Engelmann | Florian Lier | Simon Schulz | Philipp Cimiano | Friederike Eyssel | Thomas Hermann | Franz Kummert | David Schlangen | Sven Wachsmuth | Petra Wagner | Britta Wrede | Sebastian Wrede
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In order to explore intuitive verbal and non-verbal interfaces in smart environments we recorded user interactions with an intelligent apartment. Besides offering various interactive capabilities itself, the apartment is also inhabited by a social robot that is available as a humanoid interface. This paper presents a multi-modal corpus that contains goal-directed actions of naive users in attempts to solve a number of predefined tasks. Alongside audio and video recordings, our data-set consists of large amount of temporally aligned sensory data and system behavior provided by the environment and its interactive components. Non-verbal system responses such as changes in light or display contents, as well as robot and apartment utterances and gestures serve as a rich basis for later in-depth analysis. Manual annotations provide further information about meta data like the current course of study and user behavior including the incorporated modality, all literal utterances, language features, emotional expressions, foci of attention, and addressees.


A multimodal corpus for the evaluation of computational models for (grounded) language acquisition
Judith Gaspers | Maximilian Panzner | Andre Lemme | Philipp Cimiano | Katharina J. Rohlfing | Sebastian Wrede
Proceedings of the 5th Workshop on Cognitive Aspects of Computational Language Learning (CogACLL)


Engagement-based Multi-party Dialog with a Humanoid Robot
David Klotz | Johannes Wienke | Julia Peltason | Britta Wrede | Sebastian Wrede | Vasil Khalidov | Jean-Marc Odobez
Proceedings of the SIGDIAL 2011 Conference