東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Integrated Learning for Goal-Driven ...

Jaidee, Ulit.

Linked to FindBook

Google Book

Amazon

博客來

Integrated Learning for Goal-Driven Autonomy.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Integrated Learning for Goal-Driven Autonomy./
Author:	Jaidee, Ulit.
Description:	198 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.
Contained By:	Dissertation Abstracts International75-04B(E).
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3608085
ISBN:	9781303659423

Integrated Learning for Goal-Driven Autonomy.
Jaidee, Ulit.

Integrated Learning for Goal-Driven Autonomy. - 198 p.

Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.

Thesis (Ph.D.)--Lehigh University, 2013.

Goal-Driven Autonomy (GDA) is an online planning framework that focuses on the integration of planning, execution and goal reasoning. Given a goal, a GDA agent generates a plan to pursue the goal. In addition, by using its expectations, the agent reasons about what the next observed state should be when the plan's actions are executed. If the expectation does not match the observed state, the GDA agent is able to suggest a new goal to be pursued. In most GDA research, knowledge is handcrafted and later fed into the GDA agent by humans who are experts in a particular problem domain. Therefore, in this dissertation, we would like to investigate about how we can create GDA agents that have abilities to acquire knowledge by themselves and reuse that knowledge. The problem domains we focus are real-time strategy (RTS) games. We used two RTS games called DOM game and Wargus. We used Reinforcement Learning because it is an unsupervised learning method and we want our GDA agents to be autonomous. Our research went through multiple steps. First, we built a GDA agent without integration of any learning methods. Later, we incrementally integrated learning methods to each component in the GDA architecture until we build a GDA agent that could learn knowledge for all components. The experimental results show that we can create GDA agents that have the ability to acquire GDA knowledge by themselves.

ISBN: 9781303659423Subjects--Topical Terms:

1669061
Engineering, Computer.

Integrated Learning for Goal-Driven Autonomy.
LDR:02257nam a2200277 4500 001 1967908
005 20141121132943.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303659423
035 $a (MiAaPQ)AAI3608085
035 $a AAI3608085
040 $a MiAaPQ $c MiAaPQ
100 1 $a Jaidee, Ulit. $3 2105001
245 1 0 $a Integrated Learning for Goal-Driven Autonomy.
300 $a 198 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.
500 $a Adviser: Hector Munoz-Avila.
502 $a Thesis (Ph.D.)--Lehigh University, 2013.
520 $a Goal-Driven Autonomy (GDA) is an online planning framework that focuses on the integration of planning, execution and goal reasoning. Given a goal, a GDA agent generates a plan to pursue the goal. In addition, by using its expectations, the agent reasons about what the next observed state should be when the plan's actions are executed. If the expectation does not match the observed state, the GDA agent is able to suggest a new goal to be pursued. In most GDA research, knowledge is handcrafted and later fed into the GDA agent by humans who are experts in a particular problem domain. Therefore, in this dissertation, we would like to investigate about how we can create GDA agents that have abilities to acquire knowledge by themselves and reuse that knowledge. The problem domains we focus are real-time strategy (RTS) games. We used two RTS games called DOM game and Wargus. We used Reinforcement Learning because it is an unsupervised learning method and we want our GDA agents to be autonomous. Our research went through multiple steps. First, we built a GDA agent without integration of any learning methods. Later, we incrementally integrated learning methods to each component in the GDA architecture until we build a GDA agent that could learn knowledge for all components. The experimental results show that we can create GDA agents that have the ability to acquire GDA knowledge by themselves.
590 $a School code: 0105.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Artificial Intelligence. $3 769149
690 $a 0464
690 $a 0800
710 2 $a Lehigh University. $b Computer Engineering. $3 1684495
773 0 $t Dissertation Abstracts International $g 75-04B(E).
790 $a 0105
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3608085