Accepting optimally in automated negotiation with incomplete information

When a negotiating agent is presented with an offer by its opponent, it is faced with a decision: it can accept the offer that is currently on the table, or it can reject it and continue the negotiation. Both options involve an inherent risk: continuing the negotiation carries the risk of forgoing a possibly optimal offer, whereas accepting runs the risk of missing out on an even better future offer. We approach the decision of whether to accept as a sequential decision problem, by modeling the bids received as a stochastic process. We argue that this is a natural choice in the context of a negotiation with incomplete information, where the future behavior of the opponent is uncertain. We determine the optimal acceptance policies for particular opponent classes and we present an approach to estimate the expected range of offers when the type of opponent is unknown. We apply our method against a wide range of opponents, and compare its performance with acceptance mechanisms of state-of-the-art negotiation strategies. The experiments show that the proposed approach is able to find the optimal time to accept, and improves upon widely used existing acceptance mechanisms.