Browsing by Author "Razeghi, Yousef"

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Agent based negotiation for incentive driven privacy preserving information sharing
(2019-08-20) Razeghi, Yousef; Aydoğan, Reyhan; Aydoğan, Reyhan; Sözer, Hasan; Özer, A. H.; Department of Computer Science; Razeghi, Yousef
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.
Open Access
Deep reinforcement learning for acceptance strategy in bilateral negotiations
(TÜBİTAK, 2020) Razeghi, Yousef; Yavuz, Ozan; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Razeghi, Yousef; Yavuz, Ozan
This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance strategies based on predefined rules have been introduced in the automated negotiation literature. Those rules mostly rely on some heuristics, which take time and/or utility into account. For some negotiation settings, an acceptance strategy solely based on a negotiation deadline might perform well; however, it might fail in another setting. Instead of following predefined acceptance rules, this paper presents an acceptance strategy that aims to learn whether to accept its opponent's offer or make a counter offer by reinforcement signals received after performing an action. In an experimental setup, it is shown that the performance of the proposed approach improves over time.
Metadata only
Negotiation for incentive driven privacy-preserving information sharing
(Springer International Publishing, 2017) Aydoğan, Reyhan; Øzturk, P.; Razeghi, Yousef; Computer Science; AYDOĞAN, Reyhan; Razeghi, Yousef
This paper describes an agent-based, incentive-driven, and privacy-preserving information sharing framework. Main contribution of the paper 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. However, the proposed architecture is domain independent.