Abstract
Negotiation is an essential skill for agents in a multiagent system. Much work has been published on this subject, but traditional approaches assume negotiators are able to evaluate all possible deals and pick the one that is best according to some negotiation strategy. Such an approach fails when the set of possible deals is too large to analyze exhaustively. For this reason the Annual Negotiating Agents Competition of 2014 has focused on negotiations over very large agreement spaces. In this paper we present a negotiating agent that explores the search space by means of a Genetic Algorithm. It has participated in the competition successfully and finished in 2nd and 3rd place in the two categories of the competition respectively.
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Acknowledgments
This work was supported by the Agreement Technologies CONSOLIDER project, contract CSD2007-0022 and INGENIO 2010 and CHIST-ERA project ACE and EU project 318770 PRAISE.
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de Jonge, D., Sierra, C. (2016). GANGSTER: An Automated Negotiator Applying Genetic Algorithms. In: Fukuta, N., Ito, T., Zhang, M., Fujita, K., Robu, V. (eds) Recent Advances in Agent-based Complex Automated Negotiation. Studies in Computational Intelligence, vol 638. Springer, Cham. https://doi.org/10.1007/978-3-319-30307-9_14
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DOI: https://doi.org/10.1007/978-3-319-30307-9_14
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