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Countering Murphy's Law : = The use of anticipation and improvisation via an episodic memory in support of intelligent robot behavior.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Countering Murphy's Law :/
Reminder of title:	The use of anticipation and improvisation via an episodic memory in support of intelligent robot behavior.
Author:	Endo, Yoichiro.
Description:	1 online resource (263 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 70-02, Section: B, page: 1318.
Contained By:	Dissertation Abstracts International70-02B.
Subject:	Robotics. -
Online resource:	click for full text (PQDT)
ISBN:	9781109015812

Countering Murphy's Law : = The use of anticipation and improvisation via an episodic memory in support of intelligent robot behavior.
Endo, Yoichiro.

Countering Murphy's Law :The use of anticipation and improvisation via an episodic memory in support of intelligent robot behavior. - 1 online resource (263 pages)

Source: Dissertation Abstracts International, Volume: 70-02, Section: B, page: 1318.

Thesis (Ph.D.)--Georgia Institute of Technology, 2008.

Includes bibliographical references

Recently in robotics, substantial efforts have been invested on critical applications such as military, nursing, and search-and-rescue. These applications are critical in a sense that the robots may directly deal with human lives in life-or-death situations, and they are therefore required to make highly intelligent decisions as rapidly as possible. The intelligence we are looking for in this type of situations is proactiveness: the ability to anticipate and improvise.

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

Mode of access: World Wide Web

ISBN: 9781109015812Subjects--Topical Terms:

561941
Robotics.
Index Terms--Genre/Form:

554714
Electronic books.

Countering Murphy's Law : = The use of anticipation and improvisation via an episodic memory in support of intelligent robot behavior.
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100 1 $a Endo, Yoichiro. $3 1189557
245 1 0 $a Countering Murphy's Law : $b The use of anticipation and improvisation via an episodic memory in support of intelligent robot behavior.
264 0 $c 2008
300 $a 1 online resource (263 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 70-02, Section: B, page: 1318.
500 $a Adviser: Ronald C. Arkin.
502 $a Thesis (Ph.D.)--Georgia Institute of Technology, 2008.
504 $a Includes bibliographical references
520 $a Recently in robotics, substantial efforts have been invested on critical applications such as military, nursing, and search-and-rescue. These applications are critical in a sense that the robots may directly deal with human lives in life-or-death situations, and they are therefore required to make highly intelligent decisions as rapidly as possible. The intelligence we are looking for in this type of situations is proactiveness: the ability to anticipate and improvise.
520 $a Anticipation here means that the robot can assess the current situation, predict the future consequence of the situation, and execute an action to have desired outcome based on the determined assessment and prediction. On the other hand, improvisation is performed when the consequence of the situation is not fully known. In other words, it is the ability to deal with a novel situation based on knowledge or skill being acquired before.
520 $a In this dissertation, we introduce a biologically inspired computational model of proactive intelligent behavior for robots. Integrating multiple levels of machine learning techniques such as temporal difference learning, instance-based learning, and partially observable Markov decision process, aggregated episodic memories are processed in order to accomplish anticipation as well as improvisation. How this model can be implemented within a software architectural framework and integrated into a physically realized robotic system is also explained. The experimental results using a real robot and high fidelity 3D simulators are then presented in order to help us understand how extended experience of a robot influences its ability to behave proactively.
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 Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
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710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Georgia Institute of Technology. $3 845427
773 0 $t Dissertation Abstracts International $g 70-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3345973 $z click for full text (PQDT)