Automated Bilateral Multiple-issue Negotiation with No Information About Opponent
Ronghuo Zheng, Nilanjan Chakraborty, Tinglong Dai, Katia P. Sycara, Michael Lewis · 2013
In this paper, we investigate offer generation methods for automated negotiation on multiple issues with no information about the opponent's utility function. In existing negotiation literature, it is usually assumed that an agent has full information or probabilistic beliefs about the other agent's utility function. However, it is usually not possible for agents to have complete information about the other agent's preference or accurate probability distributions. We prove that using an alternating projection strategy, it is possible to reach an agreement in general automated multi-attribute negotiation, where the agents have nonlinear utility functions and no information about the utility function of the other agent. We also prove that rational agents do not have any incentive to deviate from the proposed strategy. We further present simulation results to demonstrate that the solution obtained from our protocol is quite close to the Nash bargaining solution.