USING NEURAL NETWORKS TO MINIMIZE THE DURATION OF AUTOMATED NEGOTIATION THREADS FOR HYBRID OPPONENTS
Neural networks (NNs) provide an efficient tool that can be trained to estimate the value of output parameters given certain metrics. In this paper, NNs are used to enhance intelligent agents that negotiate on behalf of their owners aiming to maximize their utility. More specifically, NNs are exploited in order to predict the hybrid negotiation behavior of the agents' opponents, thus achieving more profitable results for the parties these agents represent. The NNs provide the means so that the agents can early detect the cases where agreements are not achievable, thus supporting their decision to withdraw or not from the negotiation threads. The designed NN-assisted negotiation strategies have been evaluated via extensive experiments and are proven to be very useful, as they manage to significantly reduce the average duration of the negotiation threads.
automated negotiations mlp and gr neural networks hybrid negotiation strategies opponent behavior prediction elimination of unsuccessful negotiations