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IAMhaggler: A Negotiation Agent for Complex Environments

Part of the Studies in Computational Intelligence book series (SCI,volume 383)

Abstract

We describe the strategy used by our agent, IAMhaggler, which finished in third place in the 2010 Automated Negotiating Agent Competition. It uses a concession strategy to determine the utility level at which to make offers. This concession strategy uses a principled approach which considers the offers made by the opponent. It then uses a Pareto-search algorithm combined with Bayesian learning in order to generate a multi-issue offer with a specific utility as given by its concession strategy.

Keywords

  • Utility Level
  • Negotiation Strategy
  • Bayesian Learning
  • Automate Negotiation
  • Negotiation Agent

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Baarslag, T., Hindriks, K., Jonker, C.M., Kraus, S., Lin, R.: The first automated negotiating agents competition (ANAC 2010). In: Ito, T., et al. (eds.) New Trends in Agent-Based Complex Automated Negotiations. SCI, vol. 383, pp. 113–135. Springer, Heidelberg (2010)

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Correspondence to Colin R. Williams .

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© 2012 Springer-Verlag Berlin Heidelberg

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Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R. (2012). IAMhaggler: A Negotiation Agent for Complex Environments. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds) New Trends in Agent-Based Complex Automated Negotiations. Studies in Computational Intelligence, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24696-8_10

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  • DOI: https://doi.org/10.1007/978-3-642-24696-8_10

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