This thesis is concerned with intention recognition for a humanoid robot and investigates how
the challenges of uncertain and incomplete observations a high degree of detail of the used
models and real-time inference may be addressed by modeling the human rationale as hybrid
dynamic Bayesian networks and performing inference with these models. The key focus lies on the
automatic identification of the employed nonlinear stochastic dependencies and the
situation-specific inference.
