國立虎尾科技大學 |

Many Objective Sequential Decision Making.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Many Objective Sequential Decision Making./
Author:	Tozer, Bentz P., III.
Description:	1 online resource (122 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
Contained By:	Dissertation Abstracts International78-07B(E).
Subject:	Operations research. -
Online resource:	click for full text (PQDT)
ISBN:	9781369543711

Many Objective Sequential Decision Making.
Tozer, Bentz P., III.

Many Objective Sequential Decision Making. - 1 online resource (122 pages)

Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.

Thesis (Ph.D.)--The George Washington University, 2017.

Includes bibliographical references

Many routine tasks require an agent to perform a series of sequential actions, either to move to a desired end state or to perform a task of indefinite duration as efficiently as possible, and almost every task requires the consideration of multiple objectives, which are frequently in conflict with each other. However, methods for determining that series of actions, or policy, when considering multiple objectives have a number of issues. Some are unable to find many elements in the set of optimal policies, some are dependent on existing domain knowledge provided by an expert, and others have difficultly selecting actions as the number of objectives increases. All of these issues are limiting the use of autonomous agents to successfully complete tasks in complex, uncertain environments that are not well understood at the start of the task.

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

Mode of access: World Wide Web

ISBN: 9781369543711Subjects--Topical Terms:

573517
Operations research.
Index Terms--Genre/Form:

554714
Electronic books.

Many Objective Sequential Decision Making.
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245 1 0 $a Many Objective Sequential Decision Making.
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300 $a 1 online resource (122 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
500 $a Advisers: Thomas A. Mazzuchi; Shahram Sarkani.
502 $a Thesis (Ph.D.)--The George Washington University, 2017.
504 $a Includes bibliographical references
520 $a Many routine tasks require an agent to perform a series of sequential actions, either to move to a desired end state or to perform a task of indefinite duration as efficiently as possible, and almost every task requires the consideration of multiple objectives, which are frequently in conflict with each other. However, methods for determining that series of actions, or policy, when considering multiple objectives have a number of issues. Some are unable to find many elements in the set of optimal policies, some are dependent on existing domain knowledge provided by an expert, and others have difficultly selecting actions as the number of objectives increases. All of these issues are limiting the use of autonomous agents to successfully complete tasks in complex, uncertain environments that are not well understood at the start of the task.
520 $a This dissertation proposes the use of voting methods developed in the field of social choice theory to determine optimal policies for sequential decision making problems with many objectives, addressing limitations in methods that rely on scalarization functions and Pareto dominance to create policies. Voting methods are evaluated for action selection and policy evaluation within a model-free reinforcement learning algorithm for episodic problems ranging from two to six objectives in deterministic and stochastic environments, and compared to state of the art methods which use Pareto dominance for these tasks. The results of this analysis show that certain voting methods avoid the shortcomings of existing methods, allowing an agent to find multiple optimal policies in an initially unknown environment without any guidance from an external assistant.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Operations research. $3 573517
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The George Washington University. $b Systems Engineering. $3 1148622
773 0 $t Dissertation Abstracts International $g 78-07B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10254073 $z click for full text (PQDT)