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GANGSTER: an Automated Negotiator Applying Genetic Algorithms

AutorDe Jonge, Dave; Sierra, Carles
Palabras claveAutomated Negotiation
Non-linear utility
Genetic algorithms
Fecha de publicación2015
CitaciónRecent Advances in Agent-based Complex Automated Negotiation Volume 638 of the series Studies in Computational Intelligence, pp 225-234, 2015
ResumenNegotiation 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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