Improving agent-based meeting scheduling through preference learning.

This paper presented an autonomous Secretary Agent (SA) that can perform meeting scheduling task on behalf of their respective user through negotiations. Previous study of searching strategy uses relaxation process to allow agents negotiate by relaxing their preference when conflict arises. However...

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Main Authors: Sulaiman, Md. Nasir, Tang, En Lai, Selamat, Mohd Hasan, Muda , Zaiton
Format: Article
Language:English
Published: Asian Research Publication Network 2009
Online Access:http://psasir.upm.edu.my/id/eprint/15144/
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author Sulaiman, Md. Nasir
Tang, En Lai
Selamat, Mohd Hasan
Muda , Zaiton
author_facet Sulaiman, Md. Nasir
Tang, En Lai
Selamat, Mohd Hasan
Muda , Zaiton
author_sort Sulaiman, Md. Nasir
building UPM Institutional Repository
collection Online Access
description This paper presented an autonomous Secretary Agent (SA) that can perform meeting scheduling task on behalf of their respective user through negotiations. Previous study of searching strategy uses relaxation process to allow agents negotiate by relaxing their preference when conflict arises. However, this increased the cost of searching process. As a result, an improvement of relaxation searching strategy by adapting Neural Network (NN) learning mechanism is proposed. The back-propagation learning method is used in this research to intelligently predict the participants’ preferences and guide the host in selecting proposal s that are more likely to get accepted. Hence, higher quality solution can be found in lower communication cost. The comparison result between the proposed and two previous estimation strategies showed improvement of quality of the solution as well as the communication cost of the proposed strategy.
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institution Universiti Putra Malaysia
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spelling upm-151442013-07-22T04:56:32Z http://psasir.upm.edu.my/id/eprint/15144/ Improving agent-based meeting scheduling through preference learning. Sulaiman, Md. Nasir Tang, En Lai Selamat, Mohd Hasan Muda , Zaiton This paper presented an autonomous Secretary Agent (SA) that can perform meeting scheduling task on behalf of their respective user through negotiations. Previous study of searching strategy uses relaxation process to allow agents negotiate by relaxing their preference when conflict arises. However, this increased the cost of searching process. As a result, an improvement of relaxation searching strategy by adapting Neural Network (NN) learning mechanism is proposed. The back-propagation learning method is used in this research to intelligently predict the participants’ preferences and guide the host in selecting proposal s that are more likely to get accepted. Hence, higher quality solution can be found in lower communication cost. The comparison result between the proposed and two previous estimation strategies showed improvement of quality of the solution as well as the communication cost of the proposed strategy. Asian Research Publication Network 2009 Article PeerReviewed Sulaiman, Md. Nasir and Tang, En Lai and Selamat, Mohd Hasan and Muda , Zaiton (2009) Improving agent-based meeting scheduling through preference learning. Journal of Theoretical and Applied Information Technology, 6 (2). pp. 155-164. ISSN 1992-8645 http://www.jatit.org English
spellingShingle Sulaiman, Md. Nasir
Tang, En Lai
Selamat, Mohd Hasan
Muda , Zaiton
Improving agent-based meeting scheduling through preference learning.
title Improving agent-based meeting scheduling through preference learning.
title_full Improving agent-based meeting scheduling through preference learning.
title_fullStr Improving agent-based meeting scheduling through preference learning.
title_full_unstemmed Improving agent-based meeting scheduling through preference learning.
title_short Improving agent-based meeting scheduling through preference learning.
title_sort improving agent-based meeting scheduling through preference learning.
url http://psasir.upm.edu.my/id/eprint/15144/
http://psasir.upm.edu.my/id/eprint/15144/