Efficient Strategy Adaptation for Complex Multi-times Bilateral Negotiations

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. In the class of negotiation with some constraints, the effective automated negotiation agents can adjust their behavior depending on the characteristics of their opponents and negotiation scenarios. Recently, the attention of this study has focused on the interleaving learning with negotiation strategies from the past negotiation sessions. By analyzing the past negotiation sessions, agents can estimate the opponent's utility function based on exchanging bids. 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 pareto frontier using past negotiation sessions. In the experiments, we demonstrate that the proposed agent has better outcomes and greater search technique for the pareto frontier than existing agents in the linear and nonlinear utility functions.