An Adaptive and Optimized Negotiation Model Based on Bayesian Learning

In complex automated negotiations, a challenging issue is how to design effective learning mechanisms of agents that can deal with incomplete information, in which the agents do not know the opponent’s private information (i.e., the deadline, reservation offer, issue weight) and such information may be not unchanged. We present a time dependent, bilateral multi-issue optimized negotiation model by combining Bayesian learning with evolutionary algorithm based on mixed strategy (BLMSEAN). The proposed model defines reservation units, reservation points, and the each probability of reservation point which can be on behalf of the likelihood of the reservation point located in the unit. A regression analysis compares the correlation between estimated offers and historical offers, and Bayesian rule updates the probabilities and the weights of issues utilizing the historical offers only. The evolution algorithm with mixed mutation strategy enables the estimation to approximate more accurately opponent’s negotiation parameters and to adjust adaptively concession strategy to benefit two partners to improve the joint utility and success rate of negotiation agreement. By being evaluated empirically, this model shows its effectiveness for the agent to learn the possible range of its opponent’s private information and alter its concession strategy adaptively.