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Human robot interaction through sema...
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University of Kansas., Electrical Engineering & Computer Science.
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Human robot interaction through semantic integration of multiple modalities, dialog management, and contexts.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Human robot interaction through semantic integration of multiple modalities, dialog management, and contexts./
作者:
Johnson, David O.
面頁冊數:
265 p.
附註:
Adviser: Arvin Agah.
Contained By:
Dissertation Abstracts International69-11B.
標題:
Artificial Intelligence. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3331487
ISBN:
9780549896111
Human robot interaction through semantic integration of multiple modalities, dialog management, and contexts.
Johnson, David O.
Human robot interaction through semantic integration of multiple modalities, dialog management, and contexts.
- 265 p.
Adviser: Arvin Agah.
Thesis (Ph.D.)--University of Kansas, 2009.
The hypothesis for this research is that applying the Human Computer Interaction (HCI) concepts of using multiple modalities, dialog management, context, and semantics to Human Robot Interaction (HRI) will improve the performance of Instruction Based Learning (IBL) compared to only using speech. We tested the hypothesis by simulating a domestic robot that can be taught to clean a house using a multi-modal interface. We used a method of semantically integrating the inputs from multiple modalities and contexts that multiplies a confidence score for each input by a Fusion Weight, sums the products, and then uses the input with the highest product sum. We developed an algorithm for determining the Fusion Weights. We concluded that different modalities, contexts, and modes of dialog management impact human robot interaction; however, which combination is better depends on the importance of the accuracy of learning what is taught versus the succinctness of the dialog between the user and the robot.
ISBN: 9780549896111Subjects--Topical Terms:
769149
Artificial Intelligence.
Human robot interaction through semantic integration of multiple modalities, dialog management, and contexts.
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The hypothesis for this research is that applying the Human Computer Interaction (HCI) concepts of using multiple modalities, dialog management, context, and semantics to Human Robot Interaction (HRI) will improve the performance of Instruction Based Learning (IBL) compared to only using speech. We tested the hypothesis by simulating a domestic robot that can be taught to clean a house using a multi-modal interface. We used a method of semantically integrating the inputs from multiple modalities and contexts that multiplies a confidence score for each input by a Fusion Weight, sums the products, and then uses the input with the highest product sum. We developed an algorithm for determining the Fusion Weights. We concluded that different modalities, contexts, and modes of dialog management impact human robot interaction; however, which combination is better depends on the importance of the accuracy of learning what is taught versus the succinctness of the dialog between the user and the robot.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3331487
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