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.