Behavior learning/operating module for mobile robots

Wai Keung Fung; Yun Hui Liu

doi:10.1109/IROS.1998.724870

Behavior learning/operating module for mobile robots

Wai Keung Fung^*, Yun Hui Liu

^*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid

3 Dyfyniadau (Scopus)

Crynodeb

Robot behavior learning is an emerging research topic in robotics. By incorporating learning capability to robots, engineers are not required to hard-code appropriate actions under every possible situation. Actually, this is an impossible task. In this paper, an architecture of Behavior Learning/Operating Module (BLOM) for a robot system is proposed. In the BLOM architecture, several categories of situations and actions are formed and mappings among the situation and action categories are established. A Knight Tournament (KT) strategy is proposed for adaptive categorization of situation and action patterns in learning. A computer simulation on learning a robot behavior is also presented.

Iaith wreiddiol	Saesneg
Tudalennau	1879-1884
Nifer y tudalennau	6
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/IROS.1998.724870
Statws	Cyhoeddwyd - 1998
Cyhoeddwyd yn allanol	Ie
Digwyddiad	Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Part 1 (of 3) - Victoria, Can Hyd: 13 Hyd 1998 → 17 Hyd 1998

Cynhadledd

Cynhadledd	Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Part 1 (of 3)
Dinas	Victoria, Can
Cyfnod	13/10/98 → 17/10/98

Mynediad at Ddogfen

10.1109/IROS.1998.724870

Ffeiliau eraill a chysylltiadau

Dolen i’r cyhoeddiad yn Scopus

Dyfynnu hyn

@conference{975a3dbc637145408d1c307164ac5fde,

title = "Behavior learning/operating module for mobile robots",

abstract = "Robot behavior learning is an emerging research topic in robotics. By incorporating learning capability to robots, engineers are not required to hard-code appropriate actions under every possible situation. Actually, this is an impossible task. In this paper, an architecture of Behavior Learning/Operating Module (BLOM) for a robot system is proposed. In the BLOM architecture, several categories of situations and actions are formed and mappings among the situation and action categories are established. A Knight Tournament (KT) strategy is proposed for adaptive categorization of situation and action patterns in learning. A computer simulation on learning a robot behavior is also presented.",

author = "Fung, {Wai Keung} and Liu, {Yun Hui}",

year = "1998",

doi = "10.1109/IROS.1998.724870",

language = "English",

pages = "1879--1884",

note = "Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Part 1 (of 3) ; Conference date: 13-10-1998 Through 17-10-1998",

}

TY - CONF

T1 - Behavior learning/operating module for mobile robots

AU - Fung, Wai Keung

AU - Liu, Yun Hui

PY - 1998

Y1 - 1998

N2 - Robot behavior learning is an emerging research topic in robotics. By incorporating learning capability to robots, engineers are not required to hard-code appropriate actions under every possible situation. Actually, this is an impossible task. In this paper, an architecture of Behavior Learning/Operating Module (BLOM) for a robot system is proposed. In the BLOM architecture, several categories of situations and actions are formed and mappings among the situation and action categories are established. A Knight Tournament (KT) strategy is proposed for adaptive categorization of situation and action patterns in learning. A computer simulation on learning a robot behavior is also presented.

AB - Robot behavior learning is an emerging research topic in robotics. By incorporating learning capability to robots, engineers are not required to hard-code appropriate actions under every possible situation. Actually, this is an impossible task. In this paper, an architecture of Behavior Learning/Operating Module (BLOM) for a robot system is proposed. In the BLOM architecture, several categories of situations and actions are formed and mappings among the situation and action categories are established. A Knight Tournament (KT) strategy is proposed for adaptive categorization of situation and action patterns in learning. A computer simulation on learning a robot behavior is also presented.

UR - http://www.scopus.com/inward/record.url?scp=0032310614&partnerID=8YFLogxK

U2 - 10.1109/IROS.1998.724870

DO - 10.1109/IROS.1998.724870

M3 - Paper

AN - SCOPUS:0032310614

SP - 1879

EP - 1884

T2 - Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Part 1 (of 3)

Y2 - 13 October 1998 through 17 October 1998

ER -