Improving bilateral negotiation with evolutionary learning

This paper proposes an approach to generate offers and counter-offers in a bilateral negotiation process between cognitive agents with the learning machine capacities. The approach is configured, as each participant of a negotiation process improve their satisfaction degree, gaining knowledge from prior experience, i.e., in the next negotiation session, each participant can individually use this knowledge to redefine the configuration parameters of strategies and tactics to generate offers and counter-offers. Each agent refines their strategies using a genetic algorithm application based on the historical offers and counter-offer exchanges dataset. This context can lead to a new dimension in CSCW system development. This approach was tested in a simulated bilateral negotiation environment, which involved, for example, a buyer agent and a seller agent. The discussions about the results confront different negotiation sessions comparing agents provided with strategy and tactics reconfiguration capabilities and agents using static strategies and tactics.