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200 00$aLearning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation
210   $cKIT Scientific Publishing$d2013
215   $a1 online resource (XIV, 210 p. p.)
225 1 $aKarlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
311 08$a3-86644-952-6 
330   $aThis 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.
610   $a(Conditional) Density Estimation
610   $aDynamic Systems
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610   $aIntention Recognition
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