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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Information about the opponent is essential to improve automated negotiation strategies for bilateral multi-issue negotiation. In this paper we propose a negotiation strategy that combines a Bayesian technique to learn the preferences of an opponent during bidding and a Tit-for-Tat-like strategy to avoid exploitation by the opponent. The learned opponent model is used to achieve two important goals in negotiation. It may be used to increase the efficiency of negotiation by searching for Pareto optimal bids and to avoid exploitation by making moves that mirror the move of the other party. The performance of the proposed negotiation strategy is analyzed in a tournament setup.