Faculty of Engineering

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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, Reyhan
An 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.
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Actor-critic reinforcement learning for bidding in bilateral negotiation
(TÜBİTAK, 2022) Arslan, Furkan; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Arslan, Furkan
Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is a conflict of interests. Instead of designing a hand-crafted decision-making module, this work proposes a novel bidding strategy adopting an actor-critic reinforcement learning approach, which learns what to offer in a bilateral negotiation. An entropy reinforcement learning framework called Soft Actor-Critic (SAC) is applied to the bidding problem, and a self-play approach is employed to train the model. Our model learns to produce the target utility of the coming offer based on previous offer exchanges and remaining time. Furthermore, an imitation learning approach called behavior cloning is adopted to speed up the learning process. Also, a novel reward function is introduced that does take not only the agent’s own utility but also the opponent’s utility at the end of the negotiation. The developed agent is empirically evaluated. Thus, a large number of negotiation sessions are run against a variety of opponents selected in different domains varying in size and opposition. The agent’s performance is compared with its opponents and the performance of the baseline agents negotiating with the same opponents. The empirical results show that our agent successfully negotiates against challenging opponents in different negotiation scenarios without requiring any former information about the opponent or domain in advance. Furthermore, it achieves better results than the baseline agents regarding the received utility at the end of the successful negotiations.
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Algorithm selection and combining multiple learners for residential energy prediction
(Elsevier, 2019-10) Güngör, Onat; Akşanlı, B.; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Güngör, Onat
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 purpose, in the literature a number of prediction algorithms have been used. This work aims to increase the accuracy of those predictions as much as possible. Accordingly, we first introduce an algorithm selection approach, which identifies the best prediction algorithm for the given residence with respect to its characteristics such as number of people living, appliances and so on. In addition to this, we also study combining multiple learners to increase the accuracy of the predictions. In our experimental setup, we evaluate the aforementioned approaches. Empirical results show that adopting an algorithm selection approach performs better than any single prediction algorithm. Furthermore, combining multiple learners increases the accuracy of the energy consumption prediction significantly.
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Alternating offers protocols for multilateral negotiation
(Springer International Publishing, 2017) Aydoğan, Reyhan; Festen, D.; Hindriks, K. V.; Jonker, C. M.; Computer Science; AYDOĞAN, Reyhan
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, agents can make a bid, accept the most recent bid or walk way (i.e., end the negotiation without an agreement) when it is their turn. AMOP has two different phases: bidding and voting. The agents make their bid in the bidding phase and vote the underlying bids in the voting phase. Unlike SAOP, AMOP does not support walking away option. In both protocols, negotiation ends when the negotiating agents reach a joint agreement or some deadline criterion applies. The protocols have been evaluated empirically, showing that SAOP outperforms AMOP with the same type of conceder agents in a time-based deadline setting. SAOP was used in the ANAC 2015 competition for automated negotiating agents.
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ANAC 2017: Repeated multilateral negotiation league
(Springer, 2021) Aydoğan, Reyhan; Fujita, K.; Baarslag, T.; Jonker, C. M.; Ito, T.; Computer Science; AYDOĞAN, Reyhan
The 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.
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ANAC 2018: Repeated multilateral negotiation league
(Springer, 2020) Aydoğan, Reyhan; Fujita, K.; Baarslag, T.; Jonker, C. M.; Ito, T.; Computer Science; Ohsawa, Y.; Yada, K.; Ito, T.; Takama, Y.; Sato-Shimokawara, E.; Abe, A.; Mori, J.; Matsumura, N.; Matsumura, N.; AYDOĞAN, Reyhan
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 to develop effective negotiating agents, which can negotiate with multiple opponents more than once. This paper discusses research challenges for such negotiations as well as presenting the competition set-up and results. The results show that winner agents mostly adopt hybrid bidding strategies that take their opponents’ preferences as well as their strategy into account.
Open Access
Artificial intelligence techniques for conflict resolution
(Springer, 2021-08) Aydoğan, Reyhan; Baarslag, T.; Gerding, E.; Computer Science; AYDOĞAN, Reyhan
Conflict resolution is essential to obtain cooperation in many scenarios such as politics and business, as well as our day to day life. The importance of conflict resolution has driven research in many fields like anthropology, social science, psychology, mathematics, biology and, more recently, in artificial intelligence. Computer science and artificial intelligence have, in turn, been inspired by theories and techniques from these disciplines, which has led to a variety of computational models and approaches, such as automated negotiation, group decision making, argumentation, preference aggregation, and human-machine interaction. To bring together the different research strands and disciplines in conflict resolution, the Workshop on Conflict Resolution in Decision Making (COREDEMA) was organized. This special issue benefited from the workshop series, and consists of significantly extended and revised selected papers from the ECAI 2016 COREDEMA workshop, as well as completely new contributions.
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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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Automated negotiating agents competition (ANAC)
(AAAI press, 2017) Jonker, C. M.; Aydoğan, Reyhan; Baarslag, T.; Fujita, K.; Ito, T.; Hindiks, K.; Computer Science; AYDOĞAN, Reyhan
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 evaluation results and the protocols and strategies developed are available to the wider research community.
Open Access
Bargaining chips: Coordinating one-to-many concurrent composite negotiations
(ACM, 2021) Baarslag, T.; Elfrink, T.; Nassiri Mofakham, F.; Koça, T.; Kaisers, M.; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan
This study presents Bargaining Chips: a framework for one-to-many concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches.
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Bidding support by the pocket negotiator improves negotiation outcomes
(Springer, 2023) Aydoğan, Reyhan; Jonker, C. M.; Computer Science; AYDOĞAN, Reyhan
This paper presents the negotiation support mechanisms provided by the Pocket Negotiator (PN) and an elaborate empirical evaluation of the economic decision support (EDS) mechanisms during the bidding phase of negotiations as provided by the PN. Some of these support mechanisms are offered actively, some passively. With passive support we mean that the user only gets that support by clicking a button, whereas active support is provided without prompting. Our results show, that PN improves negotiation outcomes, counters cognitive depletion, and encourages exploration of potential outcomes. We found that the active mechanisms were used more effectively than the passive ones and, overall, the various mechanisms were not used optimally, which opens up new avenues for research. As expected, the participants with higher negotiation skills outperformed the other groups, but still they benefited from PN support. Our experimental results show that people with enough technical skills and with some basic negotiation knowledge will benefit most from PN support. Our results also show that the cognitive depletion effect is reduced by Pocket Negotiator support. The questionnaire taken after the experiment shows that overall the participants found Pocket Negotiator easy to interact with, that it made them negotiate more quickly and that it improves their outcome. Based on our findings, we recommend to 1) provide active support mechanisms (push) to nudge users to be more effective, and 2) provide support mechanisms that shield the user from mathematical complexities.
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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.
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Campaign participation prediction with deep learning
(Elsevier, 2021-08) Ayvaz, Demet; Aydoğan, Reyhan; Akçura, Munir Tolga; Şensoy, Murat; Business Administration; Computer Science; AYDOĞAN, Reyhan; AKÇURA, Münir Tolga; ŞENSOY, Murat; Ayvaz, Demet
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 offer, creating intelligent campaign management systems that can automate such decisions is essential. In addition, regulations or company policies usually restrict the number of accesses to the customers. Efficient learning of customer behaviour through dynamic campaign participation observations becomes a crucial feature that may ultimately define customer satisfaction and retention. This paper builds on the recent successes of deep learning techniques and proposes a classification model to predict customer responses for campaigns. Classic deep neural networks are good at learning hidden relations within data (i.e., patterns) but with limited capability for memorization. One solution to increase memorization is to use manually craft features, as in Wide & Deep networks, which are originally proposed for Google Play App. recommendations. We advocate using decision trees as an easier way of mining high-level relationships for enhancing Wide & Deep networks. Such an approach has the added benefit of beating manually created rules, which, most of the time, use incomplete data and have biases. A set of comprehensive experiments on campaign participation data from a leading GSM provider shows that automatically crafted features make a significant increase in the accuracy and outperform Deep and Wide & Deep models with manually crafted features.
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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The challenge of negotiation in the game of diplomacy
(Springer Nature, 2019) de Jonge, D.; Baarslag, T.; Aydoğan, Reyhan; Jonker, C.; Fujita, K.; Ito, T.; Computer Science; AYDOĞAN, Reyhan
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 to include a Diplomacy tournament within the annual Automated Negotiating Agents Competition (ANAC). In this paper we present the setup and the results of the ANAC 2017 Diplomacy Competition and the ANAC 2018 Diplomacy Challenge. We observe that none of the negotiation algorithms submitted to these two editions have been able to significantly improve the performance over a non-negotiating baseline agent. We analyze these algorithms and discuss why it is so hard to write successful negotiation algorithms for Diplomacy. Finally, we provide experimental evidence that, despite these results, coalition formation and coordination do form essential elements of the game.
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Challenges and main results of the automated negotiating agents competition (ANAC) 2019
(Springer, 2020) 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.; Computer Science; Bassiliades, N.; Chalkiadakis, G.; de Jonge, D.; AYDOĞAN, Reyhan
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. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supply-chain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league.
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Collective voice of experts in multilateral negotiation
(Springer International Publishing, 2017) Güneş, Taha Doğan; Arditi, Emir; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Güneş, Taha Doğan; Arditi, Emir
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 and what to accept during the negotiation. In the first approach namely incremental portfolio, a bid is constructed by asking each negotiation agent’s opinion in the portfolio and picking one of the suggestions stochastically considering the expertise levels of the agents. In the second approach namely crossover strategy, each expert agent makes a bid suggestion and a majority voting is used on each issue value to decide the bid content. The proposed approaches have been evaluated empirically and our experimental results showed that the crossover strategy outperformed the top five finalists of the ANAC 2016 Negotiation Competition in terms of the obtained average individual utility.
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Conflict resolution in decision making - Second international workshop, COREDEMA 2016, The Hague, The Netherlands, August 29-30, 2016, Revised Selected Papers
(Springer International Publishing, 2017) Aydoğan, Reyhan; Computer Science; Aydoğan, Reyhan; Baarslag, R.; Gerding, T.; Jonker, E.; Julian, C. M.; Sanchez-Anguix, V.; AYDOĞAN, Reyhan
Open Access
Conflict-based negotiation strategy for human-agent negotiation
(Springer, 2023-12) Keskin, Mehmet Onur; Buzcu, Berk; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Keskin, Mehmet Onur; Buzcu, Berk
Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers. However, this is not the case in human-agent negotiation in which the total number of rounds does not usually exceed tens. For this reason, an opponent model technique is needed to extract the maximum information gained with limited interaction. This study presents a conflict-based opponent modeling technique and compares its prediction performance with the well-known approaches in human-agent and automated negotiation experimental settings. According to the results of human-agent studies, the proposed model outpr erforms them despite the diversity of participants’ negotiation behaviors. Besides, the conflict-based opponent model estimates the entire bid space much more successfully than its competitors in automated negotiation sessions when a small portion of the outcome space was explored. This study may contribute to developing agents that can perceive their human counterparts’ preferences and behaviors more accurately, acting cooperatively and reaching an admissible settlement for joint interests.
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A decentralized token-based negotiation approach for multi-agent path finding
(Springer, 2021) Eran, Cihan; Keskin, Mehmet Onur; Cantürk, Furkan; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Eran, Cihan; Keskin, Mehmet Onur; Cantürk, Furkan
This paper introduces a negotiation approach to solve the Multi-Agent Path Finding problem. The approach aims to achieve a good trade-off between the privacy of the agents and the effectiveness of solutions. Accordingly, a token-based bilateral negotiation protocol and a compatible negotiation strategy are presented. The proposed approach is evaluated in a variety of scenarios by comparing it with state-of-the-art centralized approaches such as Conflict Based Search and its variant. The experimental results showed that the proposed approach can find conflict-free path solutions with a higher success rate, especially when the search space is large and high-density compared to centralized approaches while the gap between path cost differences is reasonably low. The proposed approach enables agents to have their autonomy; thus, it is convenient for MAPF problems involving self-interested agents.

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