Smart Security Guard Scheduling System Based on the Reinforcement Learning

Smart Security Guard Scheduling System Based on the Reinforcement Learning

© 2017 IEEE. The securities of some specific areas are important. This paper depicts an intelligent security guard scheduling system. The main purpose of this paper is to demonstrate a security guard system associated with study module performing better than the one with fixed patrol path. The main...

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Main Authors: Cheng Jiang, Worapan Kusakunniran, Natchanon Pomprasatpol, Chanapai Limsuwankeson, Yi Li
Other Authors: Tencent
Format: Conference or Workshop Item
Published: 2019
Subjects:
Computer Science
Online Access:https://repository.li.mahidol.ac.th/handle/123456789/45596
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Institution: Mahidol University
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spelling th-mahidol.455962019-08-23T17:55:32Z Smart Security Guard Scheduling System Based on the Reinforcement Learning Cheng Jiang Worapan Kusakunniran Natchanon Pomprasatpol Chanapai Limsuwankeson Yi Li Tencent Mahidol University Computer Science © 2017 IEEE. The securities of some specific areas are important. This paper depicts an intelligent security guard scheduling system. The main purpose of this paper is to demonstrate a security guard system associated with study module performing better than the one with fixed patrol path. The main components of this system are two levels heat map with evaporation mechanism and a saltatory path constructor. They work together to assist system scheduler in defining the patrol paths. The performance of the solution is calculated by our designed formula. In the content, the problem definition, the framework, and the comparison result are discussed in this paper. 2019-08-23T10:55:32Z 2019-08-23T10:55:32Z 2018-08-21 Conference Paper ICSEC 2017 - 21st International Computer Science and Engineering Conference 2017, Proceeding. (2018), 214-218 10.1109/ICSEC.2017.8443946 2-s2.0-85053474157 https://repository.li.mahidol.ac.th/handle/123456789/45596 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85053474157&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Computer Science
spellingShingle Computer Science
Cheng Jiang
Worapan Kusakunniran
Natchanon Pomprasatpol
Chanapai Limsuwankeson
Yi Li
Smart Security Guard Scheduling System Based on the Reinforcement Learning
description © 2017 IEEE. The securities of some specific areas are important. This paper depicts an intelligent security guard scheduling system. The main purpose of this paper is to demonstrate a security guard system associated with study module performing better than the one with fixed patrol path. The main components of this system are two levels heat map with evaporation mechanism and a saltatory path constructor. They work together to assist system scheduler in defining the patrol paths. The performance of the solution is calculated by our designed formula. In the content, the problem definition, the framework, and the comparison result are discussed in this paper.
author2 Tencent
author_facet Tencent
Cheng Jiang
Worapan Kusakunniran
Natchanon Pomprasatpol
Chanapai Limsuwankeson
Yi Li
format Conference or Workshop Item
author Cheng Jiang
Worapan Kusakunniran
Natchanon Pomprasatpol
Chanapai Limsuwankeson
Yi Li
author_sort Cheng Jiang
title Smart Security Guard Scheduling System Based on the Reinforcement Learning
title_short Smart Security Guard Scheduling System Based on the Reinforcement Learning
title_full Smart Security Guard Scheduling System Based on the Reinforcement Learning
title_fullStr Smart Security Guard Scheduling System Based on the Reinforcement Learning
title_full_unstemmed Smart Security Guard Scheduling System Based on the Reinforcement Learning
title_sort smart security guard scheduling system based on the reinforcement learning
publishDate 2019
url https://repository.li.mahidol.ac.th/handle/123456789/45596
_version_ 1763491934911856640

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