Teşvik odaklı ve mahremiyete dayalı bilgi paylaşımı için etmen temelli müzakere

While customizing their services, companies usually use their users' data. According to the new regularization, it is required to get the permission of their users to be able to store and share their users' private data. The current approaches rely on requesting access rights by providing some incentives. The customers can only accept or reject the possible incentive offered by the companies exchange for giving access rights. This thesis introduces an agent-based, incentive-driven, and privacy-preserving information sharing framework. One of the main contributions of this thesis is to give the data provider agent an active role in the information sharing process and to change the currently asymmetric position between the provider and the requester of data and information (DI) to the favor of the DI provider. Instead of a binary yes/no answer to the requester's data request and the incentive offer, the provider may negotiate about excluding from the requested DI bundle certain pieces of DI with high privacy value, and/or ask for a different type of incentive. We show the presented approach on a use case and conduct a user experiment. Questionnaire responses showed that participants like the idea of negotiation on their information sharing policies with the companies. Furthermore, this thesis proposes an acceptance strategy using deep reinforcement learning for automated negotiating agents. In the automated negotiation literature, most of the acceptance strategies are based on some predefined rules. In contrast, this thesis proposes to use reinforcement learning in order to learn when to accept opponent's offer. Our experimental evaluation shows that the developed acceptance strategy performed as well as AC-Next acceptance strategy.