A machine learning approach for mechanism selection in complex negotiations

Name: A machine learning approach for mechanism selection in complex negotiations
Author: Aydoğan, Reyhan, Marsa-Maestre, I., Klein, M., Jonker, C. M.

İsim	A machine learning approach for mechanism selection in complex negotiations
Yazar	Aydoğan, Reyhan, Marsa-Maestre, I., Klein, M., Jonker, C. M.
Basım Tarihi:	2018-04
Basım Yeri	- Springer Nature
Konu	Automated negotiation, Mechanism selection, Scenario metrics
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1004-3756
Kayıt Numarası	dde8cdcc-cfe9-4b6e-9fe5-314ef1cb59a6
Lokasyon	Computer Science
Tarih	2018-04
Notlar	ITEA M2MGrids Project ; Spanish Ministry of Economy and Competitiveness
Örnek Metin	Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the characteristics of the underlying negotiation problem (e.g. on the complexity of participant’s utility functions, as well as the degree of conflict between participants). While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by (1) defining a set of scenario metrics to capture the relevant features of negotiation problems, (2) evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, (3) applying machine learning techniques to identify which mechanisms work best with which scenarios, and (4) demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.
DOI	10.1007/s11518-018-5369-5
Cilt	27

Kaynağa git Özyeğin Üniversitesi

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Kaynağa git

A machine learning approach for mechanism selection in complex negotiations

Yazar Aydoğan, Reyhan, Marsa-Maestre, I., Klein, M., Jonker, C. M.

Basım Tarihi 2018-04

Basım Yeri - Springer Nature

Konu Automated negotiation, Mechanism selection, Scenario metrics

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1004-3756

Kayıt Numarası dde8cdcc-cfe9-4b6e-9fe5-314ef1cb59a6

Lokasyon Computer Science

Tarih 2018-04

Notlar ITEA M2MGrids Project ; Spanish Ministry of Economy and Competitiveness

Örnek Metin Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the characteristics of the underlying negotiation problem (e.g. on the complexity of participant’s utility functions, as well as the degree of conflict between participants). While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by (1) defining a set of scenario metrics to capture the relevant features of negotiation problems, (2) evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, (3) applying machine learning techniques to identify which mechanisms work best with which scenarios, and (4) demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.

DOI 10.1007/s11518-018-5369-5

Cilt 27