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Bayesian cognitive models for imitation.

Shon, Aaron P.

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Bayesian cognitive models for imitation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian cognitive models for imitation./
Author:	Shon, Aaron P.
Description:	201 p.
Notes:	Adviser: Rajesh P. N. Rao.
Contained By:	Dissertation Abstracts International68-11B.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3290590
ISBN:	9780549343349

Bayesian cognitive models for imitation.
Shon, Aaron P.

Bayesian cognitive models for imitation. - 201 p.

Adviser: Rajesh P. N. Rao.

Thesis (Ph.D.)--University of Washington, 2007.

Imitation offers a powerful mechanism for knowledge acquisition. Before human infants acquire language, much of their knowledge is gained via imitation. Increasingly, researchers hope that endowing machines with imitative abilities will bestow the flexibility, generality, and social awareness of humans. This dissertation illustrates how the interplay between cognitive studies and robotics can yield insights into both fields. Specifically, I develop several algorithms for imitation learning based on constraints suggested by cognitive results in humans. Results include simulations and implementation on a humanoid robot. I also propose a model of human social interaction based on studies of infants conducted using a humanoid robot. Given the increasing importance of probabilistic reasoning with prior knowledge in machine learning, and the mounting evidence that humans employ Bayesian models for decision making, my results are unified in a Bayesian formalism.

ISBN: 9780549343349Subjects--Topical Terms:

769149
Artificial Intelligence.

Bayesian cognitive models for imitation.
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020 $a 9780549343349
035 $a (UMI)AAI3290590
035 $a AAI3290590
040 $a UMI $c UMI
100 1 $a Shon, Aaron P. $3 1282444
245 1 0 $a Bayesian cognitive models for imitation.
300 $a 201 p.
500 $a Adviser: Rajesh P. N. Rao.
500 $a Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7451.
502 $a Thesis (Ph.D.)--University of Washington, 2007.
520 $a Imitation offers a powerful mechanism for knowledge acquisition. Before human infants acquire language, much of their knowledge is gained via imitation. Increasingly, researchers hope that endowing machines with imitative abilities will bestow the flexibility, generality, and social awareness of humans. This dissertation illustrates how the interplay between cognitive studies and robotics can yield insights into both fields. Specifically, I develop several algorithms for imitation learning based on constraints suggested by cognitive results in humans. Results include simulations and implementation on a humanoid robot. I also propose a model of human social interaction based on studies of infants conducted using a humanoid robot. Given the increasing importance of probabilistic reasoning with prior knowledge in machine learning, and the mounting evidence that humans employ Bayesian models for decision making, my results are unified in a Bayesian formalism.
590 $a School code: 0250.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Robotics. $3 1018454
690 $a 0771
690 $a 0800
690 $a 0984
710 2 $a University of Washington. $3 545923
773 0 $t Dissertation Abstracts International $g 68-11B.
790 $a 0250
790 1 0 $a Rao, Rajesh P. N., $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3290590