| Resumo: | This paper presents a new multi-agent decision support system with the purpose of aiding bilateral contract negotiators in the pre-negotiation phase, through the analysis of their possible opponents. The application area of this system is the electricity market, in which players trade a certain volume of energy at a specified price. Consequently, the main output of this system is the recommendation of the best opponent(s) to trade with and the target energy volume to trade with each of the opponents. These recommendations are achieved through the analysis of the possible opponents’ past behavior, namely by learning on their past actions. The result is the forecasting of the expected prices against each opponent depending on the volume to trade. The expected prices are then used by a game-theory based model, to reach the final decision on the best opponents to negotiate with and the ideal target volume to be negotiated with each of them
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