Intelligent agents for automated one-to-many e-commerce negotiation
ACSC '02 Proceedings of the twenty-fifth Australasian conference on Computer science - Volume 4
Concurrent bi-lateral negotiation in agent systems
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Bilateral Negotiation Decisions with Uncertain Dynamic Outside Options
WEC '04 Proceedings of the First IEEE International Workshop on Electronic Contracting
Opponent modelling in automated multi-issue negotiation using Bayesian learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
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The main question addressed in this paper is whether a theoretical outcome determined by an auction mechanism can be reasonably approximated by negotiation among agents in order to drop some of the unrealistic constraints or assumptions presupposed by the mechanism. In particular, we are interested in whether the assumption that a buyer publicly announces her preferences in order to guarantee perfect knowledge of these preferences can be dropped if a negotiating agent is used that can learn preferences. We show how to setup a multiplayer multi-issue negotiation process where preferences are learned, and we investigate how the results of this process relate to the theoretical result of holding an auction in the case of complete knowledge about the preferences of the buyer. Experiments show that the outcomes obtained by negotiating agents that learn opponent preferences approximate the outcome predicted by the mechanism. It thus follows that the assumption of perfect knowledge about buyer preferences can be removed when players are equipped with proper learning capabilities. We also investigate whether the procedure dictated by the mechanism can be further relaxed but in that case experiments indicate that more complex considerations about the market need to be taken into account.