The central aim of this thesis is the design of generic and efficient automated strategies for two-party negotiations in which negotiating parties do not reveal their preferences explicitly. A strategy for negotiation is the decision mechanism for determining the actions of a negotiator. Generic refers to the idea that the strategy needs no forehand knowledge about the opponent or the domain of negotiation. A strategy thus should be generic in the sense that it can be successfully applied to any negotiation domain and fine-tuned to domainspecific features to produce even better results. Efficiency refers to the fact that the strategy should be able to negotiate effectively against another automated agent or human negotiator and obtain an outcome that cannot be improved for both parties. The design of the negotiating strategy that is proposed in this thesis is based on analyses of the state-of- the-art negotiation strategies using an analytical method that is also proposed in this work. The method significantly extends existing negotiation benchmarks by analysing dynamic properties of a negotiation strategy. One of the main findings of the analysis, in line with the management and social science literature on negotiation [20, 23], is that the strategy should learn the opponent’s preferences in order to increase the negotiation efficiency. We applied our results in learning the opponents’ profiles in a one-to-many negotiation setting. We additionally addressed the problem of issue-dependencies. Issue dependencies form an insurmountable barrier for the state of the art negotiation strategies [9]. Therefore, we developed an approximation method to eliminate dependencies. This part of the research seems a side track, however it was fundamental that we address this problem to prove the scalability and applicability of our research results.