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210512 ||| eng |
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|a 9783866449527
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|a 1000031356
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|a Krauthausen, Peter
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|a Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation
|h Elektronische Ressource
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| 260 |
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|b KIT Scientific Publishing
|c 2013
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| 300 |
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|a 1 electronic resource (XIV, 210 p. p.)
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| 653 |
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|a Dynamic Systems
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| 653 |
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|a Intention Recognition
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|a Regularization
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| 653 |
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|a Human-Robot-Cooperation
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| 653 |
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|a (Conditional) Density Estimation
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 490 |
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|a Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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| 024 |
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|a 10.5445/KSP/1000031356
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|u https://directory.doabooks.org/handle/20.500.12854/51483
|z DOAB: description of the publication
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|u https://www.ksp.kit.edu/9783866449527
|7 0
|x Verlag
|3 Volltext
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|a 140
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|a 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.
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