İkili müzakerelerde zaman serisi tahmin modelleri

This thesis explores the dynamics of agent-based negotiations, with a focus on understanding opponent's behavior and predicting their offering patterns to make strategic decisions. Guessing the utility of the opponent's upcoming offers valuable insights for the agent's subsequent moves. The research aims to predict the opponent's future offers by employing diverse learning algorithms in various situations to measure their effectiveness in comprehending negotiation behavior. The prediction study comprises two parts; one investigating the impact of these models in one-to-one negotiations, specifically tailored for the agent's own experiences, while the other examines the performance of predictive models in a tournament setting for all agents. A learning process with three distinct targets have been established to assess the prediction models: (i) estimating the agent's utility of the opponent's next offer by considering only its offer history, (ii) estimating the agent's utility considering opponent-related variables, and (iii) estimating the opponent's utility using opponent-related variables. According to the experimented results, the best learning approach is incorporated into an agent design to observe the impacts of having predictions of future utility values on the agent's negotiation success. The thesis evaluates these models in diverse negotiation scenarios and highlights promising outcomes for the proposed methods. It also introduces a novel negotiation strategy called `Negoformer', which incorporates predictions into the offering strategy and investigates their impact on the outcome of negotiations. The experiments showcased the success of Negoformer compared to other agents in various negotiation success metrics, such as individual utility value and social welfare score.