@inproceedings{18690e0ab3354551b9ebebcaffc314a0,
title = "Collective Voice of Experts in Multilateral Negotiation",
abstract = "Inspired from the ideas such as “algorithm portfolio”, “mixture of experts”, and “genetic algorithm”, this paper presents two novel negotiation strategies, which combine multiple negotiation experts to decide what to bid and what to accept during the negotiation. In the first approach namely incremental portfolio, a bid is constructed by asking each negotiation agent{\textquoteright}s opinion in the portfolio and picking one of the suggestions stochastically considering the expertise levels of the agents. In the second approach namely crossover strategy, each expert agent makes a bid suggestion and a majority voting is used on each issue value to decide the bid content. The proposed approaches have been evaluated empirically and our experimental results showed that the crossover strategy outperformed the top five finalists of the ANAC 2016 Negotiation Competition in terms of the obtained average individual utility.",
keywords = "Agreement technologies, Automated negotiation, Multilateral negotiation, Negotiation Competition, Multi-agent systems",
author = "G{\"u}ne{\c s}, {Taha D.} and Emir Arditi and Reyhan Aydogan",
year = "2017",
doi = "10.1007/978-3-319-69131-2_27",
language = "English",
isbn = "978-3-319-69130-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "450--458",
editor = "Bo An and Ana Bazzan and Jo{\~a}o Leite and Serena Villata and {van der Torre }, Leendert",
booktitle = "PRIMA 2017: Principles and Practice of Multi-Agent Systems",
}