Browsing by Author "Baarslag, T."

Now showing 1 - 15 of 15

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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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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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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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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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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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The fifth automated negotiating agents competition (ANAC 2014)
(Springer Science+Business Media, 2016) Fujita, K.; Aydoğan, Reyhan; Baarslag, T.; Ito, T.; Jonker, C.; Computer Science; Fukuta, N.; Ito, T.; Zhang, M.; Fujita, K.; Robu, V.; AYDOĞAN, Reyhan
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 automated negotiator for scenarios where there is incomplete information about the opponent. One of the goals of this competition is to help steer the research in the area of bilateral multi-issue negotiations, and to encourage the design of generic negotiating agents that are able to operate in a variety of scenarios. 21 teams from 13 different institutes competed in ANAC 2014. This chapter describes the participating agents and the setup of the tournament, including the different negotiation scenarios that were used in the competition. We report on the results of the qualifying and final round of the tournament.
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An introduction to the pocket negotiator: a general purpose negotiation support system
(Springer International Publishing, 2017) Jonker, C. M.; Aydoğan, Reyhan; Baarslag, T.; Broekens, j.; Detweiler, C. A.; Hindriks, K. V.; Huldtgren, A.; Pasman, W.; Computer Science; AYDOĞAN, Reyhan
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 setting their preferences, estimating those of their opponent, during the bidding phase and sealing the deal. We describe the overall architecture, the essentials of the underlying techniques, the form that support takes during the negotiation phases, and we share evidence of the effectiveness of the Pocket Negotiator.
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The likeability-success tradeoff: results of the 2nd annual human-agent automated negotiating agents competition
(IEEE, 2019) Mell, J.; Gratch, J.; Aydoğan, Reyhan; Baarslag, T.; Jonker, C. M.; Computer Science; AYDOĞAN, Reyhan
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 likeability-success tradeoff in negotiations. By examining a series of repeated negotiations, actions may affect the relationship between automated negotiating agents and their human competitors over time. The results presented herein support a more complex view of human-agent negotiation and capture of integrative potential (win-win solutions). We show that, although likeability is generally seen as a tradeoff to winning, agents are able to remain well-liked while winning if integrative potential is not discovered in a given negotiation. The results indicate that the top-performing agent in this competition took advantage of this loophole by engaging in favor exchange across negotiations (cross-game logrolling). These exploratory results provide information about the effects of different submitted "black-box" agents in humanagent negotiation and provide a state-of-the-art benchmark for human-agent design.
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Optimal negotiation decision functions in time-sensitive domains
(IEEE, 2015) Baarslag, T.; Gerding, E. H.; Aydoğan, Reyhan; Schraefel, M. C.; Computer Science; AYDOĞAN, Reyhan
The last two decades have seen a growing interest in automated agents that are able to negotiate on behalf of human negotiators in a wide variety of negotiation domains. One key aspect of a successful negotiating agent is its ability to make appropriate concessions at the right time, especially when there are costs associated with the duration of the negotiation. However, so far, there is no fundamental approach on how much to concede at every stage of the negotiation in such time-sensitive domains. We introduce an efficient solution based on simultaneous search, which is able to select the optimal sequence of offers that maximizes expected payoff, given the agent's beliefs about the opponent. To this end, we show that our approach is consistent with known theoretical results and we demonstrate both its effectiveness and natural properties by applying it to a number of typical negotiation scenarios. Finally, we show in a number of experiments that our solution outperforms other state of the art strategy benchmarks.
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Results of the first annual human-agent league of the automated negotiating agents competition
(The ACM Digital Library, 2018) Mell, J.; Gratch, J.; Baarslag, T.; Aydoğan, Reyhan; Jonker, C. M.; Computer Science; AYDOĞAN, Reyhan
We present the results of the first annual Human-Agent League of ANAC. By introducing a new human-agent negotiating platform to the research community at large, we facilitated new advancements in human-aware agents. This has succeeded in pushing the envelope in agent design, and creating a corpus of useful human-agent interaction data. Our results indicate a variety of agents were submitted, and that their varying strategies had distinct outcomes on many measures of the negotiation. These agents approach the problems endemic to human negotiation, including user modeling, bidding strategy, rapport techniques, and strategic bargaining. Some agents employed advanced tactics in information gathering or emotional displays and gained more points than their opponents, while others were considered more “likeable” by their partners.
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The sixth automated negotiating agents competition (ANAC 2015)
(Springer Nature, 2017) Fujita, K.; Aydoğan, Reyhan; Baarslag, T.; Hindriks, K.; Ito, T.; Jonker, C.; Computer Science; AYDOĞAN, Reyhan
In May 2015, we organized the Sixth International Automated Negotiating Agents Competition (ANAC 2015) in conjunction with AAMAS 2015. ANAC is an international competition that challenges researchers to develop a successful automated negotiator for scenarios where there is incomplete information about the opponent. One of the goals of this competition is to help steer the research in the area of multi-issue negotiations, and to encourage the design of generic negotiating agents that are able to operate in a variety of scenarios. 24 teams from 9 different institutes competed in ANAC 2015. This chapter describes the participating agents and the setup of the tournament, including the different negotiation scenarios that were used in the competition. We report on the results of the qualifying and final round of the tournament.