Browsing Computer Science by OzU Authors "Aydoğan, Reyhan"
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Algorithm selection and combining multiple learners for residential energy prediction
Güngör, Onat; Akşanlı, B.; Aydoğan, Reyhan (Elsevier, 2019-10)Balancing supply and demand management in energy grids requires knowing energy consumption in advance. Therefore, forecasting residential energy consumption accurately plays a key role for future energy systems. For this ... -
Alternating offers protocols for multilateral negotiation
Aydoğan, Reyhan; Festen, D.; Hindriks, K. V.; Jonker, C. M. (Springer International Publishing, 2017)This paper presents a general framework for multilateral turn-taking protocols and two fully specified protocols namely Stacked Alternating Offers Protocol (SAOP) and Alternating Multiple Offers Protocol (AMOP). In SAOP, ... -
ANAC 2018: Repeated multilateral negotiation league
Aydoğan, Reyhan; Fujita, K.; Baarslag, T.; Jonker, C. M.; Ito, T. (Springer, 2020)This is an extension from a selected paper from JSAI2019. There are a number of research challenges in the field of Automated Negotiation. The Ninth International Automated Negotiating Agent Competition encourages participants ... -
Artificial intelligence tools for academic management: assigning students to academic supervisors
Sanchez-Anguix, V.; Chalumuri, R.; Alberola, J. M.; Aydoğan, Reyhan (International Academy of Technology, Education and Development (IATED), 2020)In the last few years, there has been a broad range of research focusing on how learning should take place both in the classroom and outside the classroom. Even though academic dissertations are a vital step in the academic ... -
Automated negotiating agents competition (ANAC)
Jonker, C. M.; Aydoğan, Reyhan; Baarslag, T.; Fujita, K.; Ito, T.; Hindiks, K. (AAAI press, 2017)The annual International Automated Negotiating Agents Competition (ANAC) is used by the automated negotiation research community to benchmark and evaluate its work and to challenge itself. The benchmark problems and ... -
Bottom-up approaches to achieve Pareto optimal agreements in group decision making
Sanchez-Anguix, V.; Aydoğan, Reyhan; Baarslag, T.; Jonker, C. (Springer Nature, 2019-11)In this article, we introduce a new paradigm to achieve Pareto optimality in group decision-making processes: bottom-up approaches to Pareto optimality. It is based on the idea that, while resolving a conflict in a group, ... -
Campaign participation prediction with deep learning
Ayvaz, Demet; Aydoğan, Reyhan; Akçura, Munir Tolga; Şensoy, Murat (Elsevier, 2021-08)Increasingly, on-demand nature of customer interactions put pressure on companies to build real-time campaign management systems. Instead of having managers to decide on the campaign rules, such as, when, how and whom to ... -
The challenge of negotiation in the game of diplomacy
de Jonge, D.; Baarslag, T.; Aydoğan, Reyhan; Jonker, C.; Fujita, K.; Ito, T. (Springer Nature, 2019)The game of Diplomacy has been used as a test case for complex automated negotiations for a long time, but to date very few successful negotiation algorithms have been implemented for this game. We have therefore decided ... -
Challenges and main results of the automated negotiating agents competition (ANAC) 2019
Aydoğan, Reyhan; Baarslag, T.; Fujita, K.; Mell, J.; Gratch, J.; de Jonge, D.; Mohammad, Y.; Nakadai, S.; Morinaga, S.; Osawa, H.; Aranha, C.; Jonker, C. M. (Springer, 2020)The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. ... -
Collective voice of experts in multilateral negotiation
Güneş, Taha Doğan; Arditi, Emir; Aydoğan, Reyhan (Springer International Publishing, 2017)Inspired from the ideas such as “algorithm portfolio”, “mixture of experts”, and “genetic algorithm”, this paper presents two novel negotiation strategies, which combine multiple negotiation experts to decide what to bid ... -
Conflict resolution in decision making - Second international workshop, COREDEMA 2016, The Hague, The Netherlands, August 29-30, 2016, Revised Selected Papers
Aydoğan, Reyhan (Springer International Publishing, 2017) -
Deep reinforcement learning for acceptance strategy in bilateral negotiations
Razeghi, Yousef; Yavuz, Ozan; Aydoğan, Reyhan (TÜBİTAK, 2020)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 ... -
The fifth automated negotiating agents competition (ANAC 2014)
Fujita, K.; Aydoğan, Reyhan; Baarslag, T.; Ito, T.; Jonker, C. (Springer Science+Business Media, 2016)In May 2014, we organized the Fifth International Automated Negotiating Agents Competition (ANAC 2014) in conjunction with AAMAS 2014. ANAC is an international competition that challenges researchers to develop a successful ... -
An introduction to the pocket negotiator: a general purpose negotiation support system
Jonker, C. M.; Aydoğan, Reyhan; Baarslag, T.; Broekens, j.; Detweiler, C. A.; Hindriks, K. V.; Huldtgren, A.; Pasman, W. (Springer International Publishing, 2017)The Pocket Negotiator (PN) is a negotiation support system developed at TU Delft as a tool for supporting people in bilateral negotiations over multi-issue negotiation problems in arbitrary domains. Users are supported in ... -
The likeability-success tradeoff: results of the 2nd annual human-agent automated negotiating agents competition
Mell, J.; Gratch, J.; Aydoğan, Reyhan; Baarslag, T.; Jonker, C. M. (IEEE, 2019)We present the results of the 2nd Annual Human-Agent League of the Automated Negotiating Agent Competition. Building on the success of the previous year's results, a new challenge was issued that focused exploring the ... -
A machine learning approach for mechanism selection in complex negotiations
Aydoğan, Reyhan; Marsa-Maestre, I.; Klein, M.; Jonker, C. M. (Springer Nature, 2018-04)Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A ... -
Misclassification risk and uncertainty quantification in deep classifiers
Şensoy, Murat; Saleki, Maryam; Julier, S.; Aydoğan, Reyhan; Reid, J. (IEEE, 2021)In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a ... -
A near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balance
Sanchez-Anguix, V.; Chalumuri, R.; Aydoğan, Reyhan; Julian, V. (Elsevier, 2019-03)The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work ... -
Negotiation for incentive driven privacy-preserving information sharing
Aydoğan, Reyhan; Øzturk, P.; Razeghi, Yousef (Springer International Publishing, 2017)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 ... -
Not all mistakes are equal
Şensoy, M.; Saleki, Maryam; Julier, S.; Aydoğan, Reyhan; Reid, J. (The ACM Digital Library, 2020)In many tasks, classifiers play a fundamental role in the way an agent behaves. Most rational agents collect sensor data from the environment, classify it, and act based on that classification. Recently, deep neural networks ...
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