RUcore: Rutgers University Community Repository
HELP--Human assisted Efficient Learning Protocols
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
HELP--Human assisted Efficient Learning Protocols
Identifier
ETD_2774
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056779
Language
LanguageTerm
(authority = ISO639-2);
(type = code)
eng
Genre
(authority = marcgt)
theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Electrical and Computer Engineering
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
Human-computer interaction
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Reinforcement learning
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Artificial intelligence--Educational applications
Abstract
(type = abstract)
In recent years, there has been a growing attention towards the development of artificial agents that can naturally communicate and interact with humans. The focus has primarily been on creating systems that have the ability to unify advanced learning algorithms along with various natural forms of human interaction (like providing advice, guidance, motivation, punishment, etc). However, despite the progress made, interactive systems are still directed towards researchers and scientists and consequently the everyday human is unable to exploit the potential of these systems. Another undesirable component is that in most cases, the interacting human is required to communicate with the artificial agent a large number of times, making the human often fatigued. In order to improve these systems, this thesis extends prior work and introduces novel approaches via Human-assisted Efficient Learning Protocols (HELP). Three case studies are presented that detail distinct aspects of HELP - a) representation of the task to be learned and its associated constraints, b) the efficiency of the learning algorithm used by the artificial agent and c) the unexplored “natural” modes of human interaction. The case studies will show how an artificial agent is able to efficiently learn and perform complex tasks using only a limited number of interactions with a human. Each of these studies involves human subjects interacting with a real robot and/or simulated agent to learn a particular task. The focus of HELP is to show that a machine can learn better from humans if it is given the ability to take advantage of the knowledge provided by interacting with a human partner or teacher.
PhysicalDescription
Form
(authority = gmd)
electronic resource
Extent
xi, 75 p. : ill.
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Kaushik Subramanian
Name
(ID = NAME-1);
(type = personal)
NamePart
(type = family)
Subramanian
NamePart
(type = given)
Kaushik
Role
RoleTerm
(authority = RULIB)
author
DisplayForm
Kaushik Subramanian
Name
(ID = NAME-2);
(type = personal)
NamePart
(type = family)
Gajic
NamePart
(type = given)
Zoran
Role
RoleTerm
(authority = RULIB)
chair
Affiliation
Advisory Committee
DisplayForm
Zoran Gajic
Name
(ID = NAME-3);
(type = personal)
NamePart
(type = family)
Silver
NamePart
(type = given)
Deborah
Role
RoleTerm
(authority = RULIB)
internal member
Affiliation
Advisory Committee
DisplayForm
Deborah Silver
Name
(ID = NAME-4);
(type = personal)
NamePart
(type = family)
Mandayam
NamePart
(type = given)
Narayan
Role
RoleTerm
(authority = RULIB)
internal member
Affiliation
Advisory Committee
DisplayForm
Narayan Mandayam
Name
(ID = NAME-5);
(type = personal)
NamePart
(type = family)
Littman
NamePart
(type = given)
Michael
Role
RoleTerm
(authority = RULIB)
outside member
Affiliation
Advisory Committee
DisplayForm
Michael Littman
Name
(ID = NAME-1);
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
(ID = NAME-2);
(type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm
(authority = RULIB)
school
OriginInfo
DateCreated
(qualifier = exact)
2010
DateOther
(qualifier = exact);
(type = degree)
2010
Place
PlaceTerm
(type = code)
xx
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Location
PhysicalLocation
(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T39P31DQ
Genre
(authority = ExL-Esploro)
ETD graduate
Back to the top
Rights
RightsDeclaration
(AUTHORITY = GS);
(ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder
(ID = PRH-1);
(type = personal)
Name
FamilyName
Subramanian
GivenName
Kaushik
Role
Copyright Holder
RightsEvent
(ID = RE-1);
(AUTHORITY = rulib)
Type
Permission or license
DateTime
2010-07-04 01:41:51
AssociatedEntity
(ID = AE-1);
(AUTHORITY = rulib)
Role
Copyright holder
Name
Kaushik Subramanian
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
(ID = AO-1);
(AUTHORITY = rulib)
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Back to the top
Technical
ContentModel
ETD
MimeType
(TYPE = file)
application/pdf
MimeType
(TYPE = container)
application/x-tar
FileSize
(UNIT = bytes)
2734080
Checksum
(METHOD = SHA1)
decfad4552555d210538352273eb3e0c1087b6a9
Back to the top
Statistical Profile