Sanchez-Anguix, V.; Valero Cubas, S.; Julian Inglada, VJ.; Botti Navarro, VJ.; García Fornes, AM. (2013). Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions. Information Sciences. 222:25-46. https://doi.org/10.1016/j.ins.2010.11.018
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/35735
Title:
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Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions
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Author:
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Sanchez-Anguix, Víctor
Valero Cubas, Soledad
Julian Inglada, Vicente Javier
Botti Navarro, Vicente Juan
García Fornes, Ana María
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UPV Unit:
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Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
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Issued date:
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Abstract:
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Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with ...[+]
Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with opposing preferences need to cooperate to reach their respective goals. Automated negotiation is pointed as one of the mechanisms that may provide a solution to this kind of problems. In this article, a multi-issue bilateral bargaining model for Ambient Intelligence domains is presented where it is assumed that agents have computational bounded resources and do not know their opponents' preferences. The main goal of this work is to provide negotiation models that obtain efficient agreements while maintaining the computational cost low. A niching genetic algorithm is used before the negotiation process to sample one's own utility function (self-sampling). During the negotiation process, genetic operators are applied over the opponent's and one's own offers in order to sample new offers that are interesting for both parties. Results show that the proposed model is capable of outperforming similarity heuristics which only sample before the negotiation process and of obtaining similar results to similarity heuristics which have access to all of the possible offers. (C) 2010 Elsevier Inc. All rights reserved.
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Subjects:
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Automated negotiation
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Bilateral bargaining
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Agreement technologies
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Evolutionary computation
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Multi-agent systems
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Copyrigths:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Source:
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Information Sciences. (issn:
0020-0255
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DOI:
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10.1016/j.ins.2010.11.018
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Publisher:
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Elsevier
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Publisher version:
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http://dx.doi.org/10.1016/j.ins.2010.11.018
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Project ID:
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info:eu-repo/grantAgreement/MEC//CSD2007-00022/ES/Agreement Technologies/ /
info:eu-repo/grantAgreement/MICINN//AP2008-00600/ES/AP2008-00600/
info:eu-repo/grantAgreement/MICINN//TIN2008-04446/ES/UNA PLATAFORMA PARA SISTEMAS MULTIAGENTE ABIERTOS/
info:eu-repo/grantAgreement/Generalitat Valenciana//PROMETEO08%2F2008%2F051/ES/Advances on Agreement Technologies for Computational Entities (atforce)/
info:eu-repo/grantAgreement/MICINN//TIN2009-13839-C03-01/ES/Organizaciones Virtuales Adaptativas: Arquitecturas Y Metodos De Desarrollo/
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Thanks:
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This work is supported by TIN2008-04446, PROMETEO/2008/051, TIN2009-13839-C03-01, CSD2007-00022 of the Spanish government, and FPU Grant AP2008-00600 awarded to V.Sanchez-Anguix.
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Type:
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Artículo
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