IAMhaggler: A Negotiation Agent for Complex Environments
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.
Unable to display preview. Download preview PDF.
- 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)Google Scholar
- 4.Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning. In: Proc. of the 7th Int. Joint Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 331–338 (2008)Google Scholar