語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Integrated Learning for Goal-Driven ...
~
Jaidee, Ulit.
FindBook
Google Book
Amazon
博客來
Integrated Learning for Goal-Driven Autonomy.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Integrated Learning for Goal-Driven Autonomy./
作者:
Jaidee, Ulit.
面頁冊數:
198 p.
附註:
Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.
Contained By:
Dissertation Abstracts International75-04B(E).
標題:
Engineering, Computer. -
電子資源:
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
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9262914
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入