國立虎尾科技大學 |

Planning for human -robot interaction : = Representing time and human intention.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Planning for human -robot interaction :/
其他題名:	Representing time and human intention.
作者:	Broz, Frank.
面頁冊數:	1 online resource (172 pages)
附註:	Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 6680.
Contained By:	Dissertation Abstracts International70-01B.
標題:	Robotics. -
電子資源:	click for full text (PQDT)
ISBN:	9781109785609

Planning for human -robot interaction : = Representing time and human intention.
Broz, Frank.

Planning for human -robot interaction :Representing time and human intention. - 1 online resource (172 pages)

Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 6680.

Thesis (Ph.D.)--Carnegie Mellon University, 2008.

Includes bibliographical references

This thesis proposes a novel approach to planning for a specific class of human-robot interaction domains: those in which robots engage in tasks with humans that are governed by social conventions. When humans perform these tasks, they try to achieve individual goals in an environment that they share with other people. Social conventions exist as a guideline for how to interact with others so that all parties involved can achieve their goals efficiently without interfering with one another. Recognizing what goals others are trying to achieve and performing actions at the appropriate time in the interaction are critical abilities for social competence.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781109785609Subjects--Topical Terms:

561941
Robotics.
Index Terms--Genre/Form:

554714
Electronic books.

Planning for human -robot interaction : = Representing time and human intention.
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100 1 $a Broz, Frank. $3 1189556
245 1 0 $a Planning for human -robot interaction : $b Representing time and human intention.
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300 $a 1 online resource (172 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 6680.
500 $a Advisers: Illah Nourbakhsh; Reid Simmons.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2008.
504 $a Includes bibliographical references
520 $a This thesis proposes a novel approach to planning for a specific class of human-robot interaction domains: those in which robots engage in tasks with humans that are governed by social conventions. When humans perform these tasks, they try to achieve individual goals in an environment that they share with other people. Social conventions exist as a guideline for how to interact with others so that all parties involved can achieve their goals efficiently without interfering with one another. Recognizing what goals others are trying to achieve and performing actions at the appropriate time in the interaction are critical abilities for social competence.
520 $a The approach to human-robot social interaction taken in this thesis focuses on creating more accurate models of social tasks for planning. Because the human participants are modeled as a part of the environment, the world state in these problems is dynamic and partially observable. Human intention is represented as hidden state in a partially observable Markov decision process (POMDP), and the time-dependence of action outcomes are explicitly modeled. A model structure designed by a human expert is combined with human task performance data. The resulting models are large and complex. State aggregation over the time dimension of the state space is used to trade off between the accuracy of the representation and its size in order to find sufficiently expressive models that can also be solved tractably. The utility of this approach is demonstrated by implementing a controller for a mobile robot that rides elevators with people and an agent in a driving simulator that performs the Pittsburgh left with human drivers. Performance is evaluated by comparing the policies obtained using the proposed modeling technique to policies developed using less expressive representations. In an interactions with human participants, the policies for time-dependent POMDP models with human intention as hidden state outperform the other policies, achieving both higher rewards and more positive evaluations for naturalness and social propriety of behavior.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Robotics. $3 561941
650 4 $a Artificial intelligence. $3 559380
650 4 $a Social psychology. $3 554804
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710 2 $a Carnegie Mellon University. $3 845406
773 0 $t Dissertation Abstracts International $g 70-01B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3345274 $z click for full text (PQDT)