Otomatikleştirilmiş ikili pazarlıklarda ilişkisel ve frekansçı rakip modelleme yaklaşımları

This thesis mainly focuses on the problem of learning opponent's preferences during the negotiation in bilateral automated negotiation in which agents negotiate with each other to reach an agreement. Accordingly, it addresses the problems with the classical frequentist approach and advances the state-of-the-art in opponent modeling in automated negotiation by introducing a novel frequency opponent modeling mechanism, which updates some of the assumptions introduced by classical frequency approaches. Moreover, this thesis also proposes adopting association rule mining techniques to learn the opponent's preferences in bilateral negotiation. An extensive evaluation of those proposed approaches shows that the proposed approaches outperform the classical frequency model. In addition, this thesis argues that while optimizing one's utility function is essential, agents in a society should not ignore the opponent's utility in the final agreement to improve the agent's long-term interests in the system. It aims to show whether or not it is possible to design a social agent (i.e., one that aims to optimize both sides' utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent's behavior and the state of the negotiation. The results show that the proposed social agent does not only maximize social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies.