LEADER 01792nam  2200373z- 450 
001 9910346889403321
005 20210211
010   $a1000031356
035   $a(CKB)4920000000101628
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/51483
035   $a(oapen)doab51483
035   $a(EXLCZ)994920000000101628
100   $a20202102d2013      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$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
610   $aHuman-Robot-Cooperation
610   $aIntention Recognition
610   $aRegularization
700   $aKrauthausen$b Peter$4auth$01305984
906   $aBOOK
912   $a9910346889403321
996   $aLearning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation$93028101
997   $aUNINA