Browsing by Author "Sanchez-Anguix, V."

Now showing 1 - 7 of 7

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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, Reyhan
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 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.
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Bottom-up approaches to achieve Pareto optimal agreements in group decision making
(Springer Nature, 2019-11) Sanchez-Anguix, V.; Aydoğan, Reyhan; Baarslag, T.; Jonker, C.; Computer Science; AYDOĞAN, Reyhan
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, individuals may trust some members more than others; thus, they may be willing to cooperate and share more information with those members. Therefore, one can divide the group into subgroups where more cooperative mechanisms can be formed to reach Pareto optimal outcomes. This is the first work that studies such use of a bottom-up approach to achieve Pareto optimality in conflict resolution in groups. First, we prove that an outcome that is Pareto optimal for subgroups is also Pareto optimal for the group as a whole. Then, we empirically analyze the appropriate conditions and achievable performance when applying bottom-up approaches under a wide variety of scenarios based on real-life datasets. The results show that bottom-up approaches are a viable mechanism to achieve Pareto optimality with applications to group decision-making, negotiation teams, and decision making in open environments.
Open Access
Can social agents efficiently perform in automated negotiation?
(MDPI, 2021-07) Sanchez-Anguix, V.; Tunalı, O.; Aydoğan, Reyhan; Julian, V.; Computer Science; AYDOĞAN, Reyhan
In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents in a society should not ignore the opponent’s utility in the final agreement to improve the agent’s long-term perspectives in the system. This article aims to show whether it is possible to design a social agent (i.e., one that aims to optimize both sides’ utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent’s behavior and the state of the negotiation. The results show that the proposed social agent not only maximizes social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies.
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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, Reyhan
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 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.
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Predicting shuttle arrival time in istanbul
(Springer Nature, 2020) Çoban, Selami; Sanchez-Anguix, V.; Aydoğan, Reyhan; Computer Science; Herrera, F.; Matsui, K.; Rodriguez Gonzalez, S.; AYDOĞAN, Reyhan; Çoban, Selami
Nowadays, transportation companies look for smart solutions in order to improve quality of their services. Accordingly, an intercity bus company in Istanbul aims to improve their shuttle schedules. This paper proposes revising scheduling of the shuttles based on their estimated travel time in the given timeline. Since travel time varies depending on the date of travel, weather, distance, we present a prediction model using both travel history and additional information such as distance, holiday, and weather. The results showed that Random Forest algorithm outperformed other methods and adding additional features increased its accuracy rate.
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Preface
(Springer, 2023) Aydoğan, Reyhan; Criado, N.; Lang, J.; Sanchez-Anguix, V.; Serramia, M.; Computer Science; AYDOĞAN, Reyhan
N/A
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Rethinking frequency opponent modeling in automated negotiation
(Springer International Publishing, 2017) Tunalı, Okan; Aydoğan, R.; Sanchez-Anguix, V.; Tunalı, Okan
Frequency opponent modeling is one of the most widely used opponent modeling techniques in automated negotiation, due to its simplicity and its good performance. In fact, it outperforms even more complex mechanisms like Bayesian models. Nevertheless, the classical frequency model does not come without its own assumptions, some of which may not always hold in many realistic settings. This paper advances the state of the art in opponent modeling in automated negotiation by introducing a novel frequency opponent modeling mechanism, which soothes some of the assumptions introduced by classical frequency approaches. The experiments show that our proposed approach outperforms the classic frequency model in terms of evaluation of the outcome space, estimation of the Pareto frontier, and accuracy of both issue value evaluation estimation and issue weight estimation.