Person: AYDOĞAN, Reyhan
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Reyhan
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AYDOĞAN
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ArticlePublication Open Access The effect of appearance of virtual agents in human-agent negotiation(MDPI, 2022-09) Türkgeldi, Berkay; Özden, Cana Su; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Türkgeldi, Berkay; Özden, Cana SuArtificial Intelligence (AI) changed our world in various ways. People start to interact with a variety of intelligent systems frequently. As the interaction between human and AI systems increases day by day, the factors influencing their communication have become more and more important, especially in the field of human-agent negotiation. In this study, our aim is to investigate the effect of knowing your negotiation partner (i.e., opponent) with limited knowledge, particularly the effect of familiarity with the opponent during human-agent negotiation so that we can design more effective negotiation systems. As far as we are aware, this is the first study investigating this research question in human-agent negotiation settings. Accordingly, we present a human-agent negotiation framework and conduct a user experiment in which participants negotiate with an avatar whose appearance and voice are a replica of a celebrity of their choice and with an avatar whose appearance and voice are not familiar. The results of the within-subject design experiment show that human participants tend to be more collaborative when their opponent is a celebrity avatar towards whom they have a positive feeling rather than a non-celebrity avatar.
ArticlePublication Metadata only A near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balance(Elsevier, 2019-03) Sanchez-Anguix, V.; Chalumuri, R.; Aydoğan, Reyhan; Julian, V.; Computer Science; AYDOĞAN, ReyhanThe 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 from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors' preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the studentsupervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time.Conference ObjectPublication Metadata only The 13th international automated negotiating agent competition challenges and results(Springer, 2023) Aydoğan, Reyhan; Baarslag, T.; Fujita, K.; Hoos, H. H.; Jonker, C. M.; Mohammad, Y.; Renting, B. M.; Computer Science; AYDOĞAN, ReyhanAn international competition for negotiating agents has been organized for years to facilitate research in agent-based negotiation and to encourage the design of negotiating agents that can operate in various scenarios. The 13th International Automated Negotiating Agents Competition (ANAC 2022) was held in conjunction with IJCAI2022. In ANAC2022, we had two leagues: Automated Negotiation League (ANL) and Supply Chain Management League (SCML). For the ANL, the participants designed a negotiation agent that can learn from the previous bilateral negotiation sessions it was involved in. In contrast, the research challenge was to make the right decisions to maximize the overall profit in a supply chain environment, such as determining with whom and when to negotiate. This chapter describes the overview of ANL and SCML in ANAC2022, and reports the results of each league, respectively.ArticlePublication Open Access Using convolutional neural networks to automate aircraft maintenance visual inspection(MDPI, 2020-12) Doğru, Anıl; Bouarfa, S.; Arizar, R.; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Doğru, AnılConvolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F-1 and F-2 scores were 62.67% and 59.35%, respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approach uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance with an F-1 score of (67.50%) and F-2 score of (66.37%).ArticlePublication Open Access Formal modelling and verification of a multi-agent negotiation approach for airline operations control(Springer, 2021-02-12) Bouarfa, S.; Aydoğan, Reyhan; Sharpanskykh, A.; Computer Science; AYDOĞAN, ReyhanThis paper proposes and evaluates a new airline disruption management strategy using multi-agent system modelling, simulation, and verification. This new strategy is based on a multi-agent negotiation protocol and is compared with three airline strategies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. To evaluate the new strategy, a rule-based multi-agent system model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation strategy outperforms the established strategies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.EditorialPublication Metadata only Preface(Springer, 2021) Aydoğan, Reyhan; Ito, T.; Moustafa, A.; Otsuka, T.; Zhang, M.; Computer Science; AYDOĞAN, ReyhanN/AConference ObjectPublication Metadata only Artificial intelligence tools for academic management: assigning students to academic supervisors(International Academy of Technology, Education and Development (IATED), 2020) Sanchez-Anguix, V.; Chalumuri, R.; Alberola, J. M.; Aydoğan, Reyhan; Computer Science; Chova, L. G.; Martinez, A. L.; Torres, I. C.; AYDOĞAN, ReyhanIn 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 life of both students, as they get to employ all their knowledge and skills in an original project, there has been limited research on this topic. In this paper we explore the topic of allocating students to supervisors, a time-consuming and complex task faced by many academic departments across the world. Firstly, we discuss the advantages and disadvantages of employing different allocation strategies from the point of view of students and supervisors. Then, we describe an artificial intelligence tool that overcomes many of the limitations of the strategies described in the article, and that solves the problem of allocating students to supervisors. The tool is capable of allocating students to supervisors by considering the preferences of both students and supervisors with regards to research topics, the maximum supervision quota of supervisors, and the workload balance of supervisors.ArticlePublication Metadata only Nova: Value-based negotiation of norms(ACM, 2021-08-01) Aydoğan, Reyhan; Kafali, Ö.; Jonker, C. M.; Singh, M. P.; Computer Science; AYDOĞAN, Reyhan; Arslan, FurkanSpecifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent revises the nMAS specification to promote its values by executing a set of norm revision rules that incorporate ontology-based reasoning. To demonstrate that our framework supports creating a transparent and accountable nMAS specification, we conduct an experiment with human participants who negotiate against our agent. Our findings show that our negotiation agent reaches better agreements (with small p-value and large effect size) faster than a baseline strategy. Moreover, participants perceive that our agent enables more collaborative and transparent negotiations than the baseline (with small p-value and large effect size in particular settings) toward reaching an agreement.Conference ObjectPublication Metadata only ANAC 2017: Repeated multilateral negotiation league(Springer, 2021) Aydoğan, Reyhan; Fujita, K.; Baarslag, T.; Jonker, C. M.; Ito, T.; Computer Science; AYDOĞAN, ReyhanThe Automated Negotiating Agents Competition (ANAC) is annually organized competition to facilitate the research on automated negotiation. This paper presents the ANAC 2017 Repeated Multilateral Negotiation League. As human negotiators do, agents are supposed to learn from their previous negotiations and improve their negotiation skills over time. Especially, when they negotiate with the same opponent on the same domain, they can adopt their negotiation strategy according to their past experiences. They can adjust their acceptance threshold or bidding strategy accordingly. In ANAC 2017, participants aimed to develop such a negotiating agent. Accordingly, this paper describes the competition settings and results with a brief description of the winner negotiation strategies.Conference ObjectPublication Metadata only Not all mistakes are equal(The ACM Digital Library, 2020) Şensoy, M.; Saleki, Maryam; Julier, S.; Aydoğan, Reyhan; Reid, J.; Computer Science; AYDOĞAN, Reyhan; Saleki, MaryamIn 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 (DNNs) have become the dominant approach to develop classifiers due to their excellent performance. When training and evaluating the performance of DNNs, it is normally assumed that the cost of all misclassification errors are equal. However, this is unlikely to be true in practice. Incorrect classification predictions can cause an agent to take inappropriate actions. The costs of these actions can be asymmetric, vary from agent-to-agent, and depend on context. In this paper, we discuss the importance of considering risk and uncertainty quantification together to reduce agents' cost of making misclassifications using deep classifiers.