東華大學圖書館 |

語系: 繁體中文

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

Representation, Planning, and Learni...

Liemhetcharat, Somchaya.

FindBook

Google Book

Amazon

博客來

Representation, Planning, and Learning of Dynamic Ad Hoc Robot Teams.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Representation, Planning, and Learning of Dynamic Ad Hoc Robot Teams./
作者:	Liemhetcharat, Somchaya.
面頁冊數:	157 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
Contained By:	Dissertation Abstracts International75-02B(E).
標題:	Engineering, Robotics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3575512
ISBN:	9781303536724

Representation, Planning, and Learning of Dynamic Ad Hoc Robot Teams.
Liemhetcharat, Somchaya.

Representation, Planning, and Learning of Dynamic Ad Hoc Robot Teams. - 157 p.

Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2013.

Forming an effective multi-robot team to perform a task is a key problem in many domains. The performance of a multi-robot team depends on the robots the team is composed of, where each robot has different capabilities. Team performance has previously been modeled as the sum of single-robot capabilities, and these capabilities are assumed to be known.

ISBN: 9781303536724Subjects--Topical Terms:

1018454
Engineering, Robotics.

Representation, Planning, and Learning of Dynamic Ad Hoc Robot Teams.
LDR:03827nam a2200337 4500 001 1960559
005 20140623111235.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303536724
035 $a (MiAaPQ)AAI3575512
035 $a AAI3575512
040 $a MiAaPQ $c MiAaPQ
100 1 $a Liemhetcharat, Somchaya. $3 2096245
245 1 0 $a Representation, Planning, and Learning of Dynamic Ad Hoc Robot Teams.
300 $a 157 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
500 $a Adviser: Manuela Veloso.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2013.
520 $a Forming an effective multi-robot team to perform a task is a key problem in many domains. The performance of a multi-robot team depends on the robots the team is composed of, where each robot has different capabilities. Team performance has previously been modeled as the sum of single-robot capabilities, and these capabilities are assumed to be known.
520 $a Is team performance just the sum of single-robot capabilities? This thesis is motivated by instances where agents perform differently depending on their teammates, i.e., there is synergy in the team. For example, in human sports teams, a well-trained team performs better than an all-stars team composed of top players from around the world. This thesis introduces a novel model of team synergy---the Synergy Graph model---where the performance of a team depends on each robot's individual capabilities and a task-based relationship among them.
520 $a Robots are capable of learning to collaborate and improving team performance over time, and this thesis explores how such robots are represented in the Synergy Graph Model. This thesis contributes a novel algorithm that allocates training instances for the robots to improve, so as to form an effective multi-robot team.
520 $a The goal of team formation is the optimal selection of a subset of robots to perform the task, and this thesis contributes team formation algorithms that use a Synergy Graph to form an effective multi-robot team with high performance. In particular, the performance of a team is modeled with a Normal distribution to represent the nondeterminism of the robots' actions in a dynamic world, and this thesis introduces the concept of a delta-optimal team that trades off risk versus reward. Further, robots may fail from time to time, and this thesis considers the formation of a robust multi-robot team that attains high performance even if failures occur.
520 $a This thesis considers ad hoc teams, where the robots of the team have not collaborated together, and so their capabilities and synergy are initially unknown. This thesis contributes a novel learning algorithm that uses observations of team performance to learn a Synergy Graph that models the capabilities and synergy of the team. Further, new robots may become available, and this thesis introduces an algorithm that iteratively updates a Synergy Graph with new robots.
520 $a This thesis validates the Synergy Graph model in extensive simulations and on real robots, such as the NAO humanoid robots, CreBots, and Lego Mindstorms NXTs. These robots vary in terms of their locomotion type, sensor capabilities, and processing power, and show that the Synergy Graph model is general and applicable to a wide range of robots. In the empirical evaluations, this thesis demonstrates the effectiveness of the Synergy Graph representation, planning, and learning in a rich spectrum of ad hoc team formation scenarios.
590 $a School code: 0041.
650 4 $a Engineering, Robotics. $3 1018454
650 4 $a Computer Science. $3 626642
690 $a 0771
690 $a 0984
710 2 $a Carnegie Mellon University. $b Robotics. $3 2096237
773 0 $t Dissertation Abstracts International $g 75-02B(E).
790 $a 0041
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3575512