The Learning of an Opponent’s Approximate Preferences in Bilateral Automated Negotiation

The Learning of an Opponent’s Approximate Preferences in Bilateral Automated Negotiation

Autonomous agents can negotiate on behalf of buyers and sellers to make a contract in the e-marketplace. In bilateral negotiation, they need to find a joint agreement by satisfying each other. That is, an agent should learn its opponent’s preferences. However, the agent has limited time to...

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Autores principales: Jazayeriy,Hamid, Azmi-Murad,Masrah, Sulaiman,Nasir, Izura Udizir,Nur
Lenguaje:English
Publicado: Universidad de Talca 2011
Materias:
Bilateral negotiation
Learning preferences
Uncertain information
Genetic algorithm
E-marketplace
Acceso en línea:http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762011000300006
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spelling oai:scielo:S0718-187620110003000062018-10-12The Learning of an Opponent’s Approximate Preferences in Bilateral Automated NegotiationJazayeriy,HamidAzmi-Murad,MasrahSulaiman,NasirIzura Udizir,Nur Bilateral negotiation Learning preferences Uncertain information Genetic algorithm E-marketplace Autonomous agents can negotiate on behalf of buyers and sellers to make a contract in the e-marketplace. In bilateral negotiation, they need to find a joint agreement by satisfying each other. That is, an agent should learn its opponent’s preferences. However, the agent has limited time to find an agreement while trying to protect its payoffs by keeping its preferences private. In doing so, generating offers with incomplete information about the opponent’s preferences is a complex process and, therefore, learning these preferences in a short time can assist the agent to generate proper offers. In this paper, we have developed an incremental on-line learning approach by using a hybrid soft-computing technique to learn the opponent’s preferences. In our learning approach, first, the size of possible preferences is reduced by encoding the uncertain preferences into a series of fuzzy membership functions. Then, a simplified genetic algorithm is used to search the best fuzzy preferences that articulate the opponent’s intention. Experimental results showed that our learning approach can estimate the opponent’s preferences effectively. Moreover, results indicate that agents which use the proposed learning approach not only have more chances to reach agreements but also will be able to find agreements with greater joint utility.info:eu-repo/semantics/openAccessUniversidad de TalcaJournal of theoretical and applied electronic commerce research v.6 n.3 20112011-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762011000300006en10.4067/S0718-18762011000300006
institution Scielo Chile
collection Scielo Chile
language English
topic Bilateral negotiation
Learning preferences
Uncertain information
Genetic algorithm
E-marketplace
spellingShingle Bilateral negotiation
Learning preferences
Uncertain information
Genetic algorithm
E-marketplace
Jazayeriy,Hamid
Azmi-Murad,Masrah
Sulaiman,Nasir
Izura Udizir,Nur
The Learning of an Opponent’s Approximate Preferences in Bilateral Automated Negotiation
description Autonomous agents can negotiate on behalf of buyers and sellers to make a contract in the e-marketplace. In bilateral negotiation, they need to find a joint agreement by satisfying each other. That is, an agent should learn its opponent’s preferences. However, the agent has limited time to find an agreement while trying to protect its payoffs by keeping its preferences private. In doing so, generating offers with incomplete information about the opponent’s preferences is a complex process and, therefore, learning these preferences in a short time can assist the agent to generate proper offers. In this paper, we have developed an incremental on-line learning approach by using a hybrid soft-computing technique to learn the opponent’s preferences. In our learning approach, first, the size of possible preferences is reduced by encoding the uncertain preferences into a series of fuzzy membership functions. Then, a simplified genetic algorithm is used to search the best fuzzy preferences that articulate the opponent’s intention. Experimental results showed that our learning approach can estimate the opponent’s preferences effectively. Moreover, results indicate that agents which use the proposed learning approach not only have more chances to reach agreements but also will be able to find agreements with greater joint utility.
author Jazayeriy,Hamid
Azmi-Murad,Masrah
Sulaiman,Nasir
Izura Udizir,Nur
author_facet Jazayeriy,Hamid
Azmi-Murad,Masrah
Sulaiman,Nasir
Izura Udizir,Nur
author_sort Jazayeriy,Hamid
title The Learning of an Opponent’s Approximate Preferences in Bilateral Automated Negotiation
title_short The Learning of an Opponent’s Approximate Preferences in Bilateral Automated Negotiation
title_full The Learning of an Opponent’s Approximate Preferences in Bilateral Automated Negotiation
title_fullStr The Learning of an Opponent’s Approximate Preferences in Bilateral Automated Negotiation
title_full_unstemmed The Learning of an Opponent’s Approximate Preferences in Bilateral Automated Negotiation
title_sort learning of an opponent’s approximate preferences in bilateral automated negotiation
publisher Universidad de Talca
publishDate 2011
url http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762011000300006
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