Abstract
Bilateral multi-issue closed negotiation is an important class for real-life negotiations. Usually, negotiation problems have constraints such as a complex and unknown opponent's utility in real time, or time discounting. Recently, the attention of this study has focused on the nonlinear utility functions. In nonlinear utility functions, most of the negotiation strategies for linear utility functions can't adopt to the scenarios of nonlinear utility functions. In this paper, we propose an automated agent that estimates the opponent's strategies based on the past negotiation sessions. Our agent tries to compromise to the estimated maximum utility of the opponent by the end of the negotiation. In addition, our agent can adjust the speed of compromise by judging the opponent's Thomas-Kilmann Conflict Mode and search for the pare to frontier using past negotiation sessions. In the experiments, we demonstrate that the proposed agent has better outcomes and greater search technique than existing agents.