A TWO-STAGE WINWIN MULTIATTRIBUTE NEGOTIATION MODEL: OPTIMIZATION AND THEN CONCESSION
Many automated negotiation models have been developed to solve the conflict in many distributed computational systems. However, the problem of finding winwin outcome in multiattribute negotiation has not been tackled well. To address this issue, based on an evolutionary method of multiobjective optimization, this paper presents a negotiation model that can find winwin solutions of multiple attributes, but needs not to reveal negotiating agents private utility functions to their opponents or a third-party mediator. Moreover, we also equip our agents with a general type of utility functions of interdependent multiattributes, which captures human intuitions well. In addition, we also develop a novel time-dependent concession strategy model, which can help both sides find a final agreement among a set of winwin ones. Finally, lots of experiments confirm that our negotiation model outperforms the existing models developed recently. And the experiments also show our model is stable and efficient in finding fair winwin outcomes, which is seldom solved in the existing models.
automated negotiation agent multiattribute decision making e-business evolutionary computing