The work in this research direction contributes to the state-of-the-art in research areas such as social robotics, human-machine interaction and multi-agent systems. High-level semantic representations enables us to create interaction and collaboration between humans and robots, virtual characters, software and simulated agents. Our focus lies not just on creating those effective and intuitive interactive setups but also on evaluating them and predicting the effects of the interaction. The tools that we apply range from empirically grounded usability analysis to multi-actor systems and agent-based simulation.
In the area of interaction analysis, qualitative and quantitative tools of varying complexity which study relevant aspects in the particular application domain are applied. Examples taken from the mobile robotic telepresence systems domain include proxemics (spatial relationships), sociometry (conversation characteristics), presence, usability, and non-verbal cues (e.g. facial expressions and glare). Other examples taken from the domain of sensor networks for home environments include usage statistics and behavioral changes.
Interaction plays a central role in multi-agent systems. When and how actors interact determines the actual outcome of the overall system. Using agent-based simulation, we analyze complex socio-technical systems (e.g. in logistics) or predict future development of complex systems comprising diverse actors. Our research hereby relates to engineering complex models with theory and data-driven approaches. Frameworks for capturing complex interaction, for human immersion into simulated worlds as well as for sensor-based alignment of a running simulation to real-world dynamics form the current focus of research.
- interaction quality
- development of evaluation methods
- multi-actor systems
- agent-based simulation