The board game Diplomacy is considered one of the most challenging test cases for automated negotiation. While many bots have been developed for this game, very few of them are able to negotiate successfully, and the ones that do, have been trained on large data sets of human example games. This makes it hard to apply the same techniques to other games or negotiation scenarios for which no human knowledge is (yet) available. Furthermore, since those bots were trained using deep learning, they are essentially black-boxes for which it is hard to understand how they work. So, these bots do not help us much in gaining a better understanding of strong negotiation techniques. Therefore, in this paper we present a new Diplomacy bot, called Attila, that is purely based on symbolic A.I. Its negotiation algorithm makes use of an existing oracle for the tactical part of the game, called the ‘D-Brane Tactical Module’ (DBTM). We explain how the DBTM can be converted into a search algorithm for automated negotiation, and we present experiments that show that Attila strongly outperforms several state-of-the-art Diplomacy bots.