Abstract: Software based intelligent agents performing automated negotiations for the generation of offers leading to settlements that maximize the utility. A set of opponent's models, which best represents the opponent's profile, behavior, and cognitive orientation, is paired to a set of hypothetical agents. The hypothetical agent and the associated opponent model bargains against each other to generate a hypothetical sequence of proposed agent's offers and predicted opponent's counter offers. An ensemble unifies the proposed and predicted sequences based on the current and past performance measured according to the accuracy of the opponent's behavior prediction and agent's utility maximization. The opponent's adopted tactic is dynamically learned during the negotiation to update the opponent's model. The initial belief about the probability distribution of the opponent's preferences and internal states is set according to the historical negotiation's data.

Abstract: Software based intelligent agents performing automated negotiations for the generation of offers leading to settlements that maximize the utility. A set of opponent's models, which best represents the opponent's profile, behavior, and cognitive orientation, is paired to a set of hypothetical agents. The hypothetical agent and the associated opponent model bargains against each other to generate a hypothetical sequence of proposed agent's offers and predicted opponent's counter offers. An ensemble unifies the proposed and predicted sequences based on the current and past performance measured according to the accuracy of the opponent's behavior prediction and agent's utility maximization. The opponent's adopted tactic is dynamically learned during the negotiation to update the opponent's model. The initial belief about the probability distribution of the opponent's preferences and internal states is set according to the historical negotiation's data.