GANGSTER: An automated negotiator applying genetic algorithms

de Jonge, D; Sierra, C

GANGSTER: An automated negotiator applying genetic algorithms

de Jonge, D Sierra, C

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Publication Type:: Conference Proceeding
Citation:: Studies in Computational Intelligence, 2016, 638 pp. 225 - 234
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	de Jonge, D	en_US
dc.contributor.author	Sierra, C https://orcid.org/0000-0003-0839-6233	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Studies in Computational Intelligence, 2016, 638 pp. 225 - 234	en_US
dc.identifier.isbn	9783319303055	en_US
dc.identifier.issn	1860-949X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/103349
dc.description.abstract	© Springer International Publishing Switzerland 2016. 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.	en_US
dc.relation.ispartof	Studies in Computational Intelligence	en_US
dc.relation.isbasedon	10.1007/978-3-319-30307-9_14	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	GANGSTER: An automated negotiator applying genetic algorithms	en_US
dc.type	Conference Proceeding
utslib.citation.volume	638	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	638	en_US

Abstract:

© Springer International Publishing Switzerland 2016. 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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http://hdl.handle.net/10453/103349