An innovative approach for predicting both negotiation deadline and utility in multi-issue negotiation
In agent negotiation, agents usually need to know their opponents' negotiation parameters (i.e preference, deadline, reservation utility) to effectively adjust their negotiation strategies, thus an agreement can be reached. However, in a competitive negotiation environment, agents may not be willing to reveal their negotiation parameters, which increases the difficulty of reaching an agreement. In order to solve this problem, agents need to have the learning ability to predict their opponents' negotiation parameters. In this paper, a Bayesian-based prediction approach is proposed to help an agent to predict its opponent's negotiation deadline and reservation utility in bilateral multi-issue negotiation. Besides, a concession strategy adjustment algorithm is integrated into the proposed prediction approach to improve the negotiation result. The experimental results indicate that the proposed approach can increase the profit and the success rate of bilateral multi-issue negotiation.
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