research-article

The Automated Negotiating Agents Competition, 2010–2015

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Catholijn M. JonkerAuthors Info & Claims

AI Magazine, Volume 36, Issue 4

Pages 115 - 118

https://doi.org/10.1609/aimag.v36i4.2609

Published: 01 December 2015 Publication History

Abstract

The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state of the art in automated multi‐issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, and bilateral and multilateral protocols. Two of the challenges that remain are how to develop argumentation‐based negotiation agents that, in addition to making offers, can inform and argue to obtain an acceptable agreement for both parties; and how to create agents that can negotiate in a human fashion.

References

[1]

Baarslag, T., Fujita, K., Gerding, E. H., Hindriks, K., Ito, T., Jennings, N. R., Jonker, C., Kraus, S., Lin, R., Robu, V.; and Williams, C. R. 2013. Evaluating Practical NegotiatingAgents: Results and Analysis of the 2011 International Competition. Artificial Intelligence 198(May): 73–103, May 2013.

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[2]

Baarslag, T., Hindriks, K., Jonker, C. M., Kraus, S.; and Lin, R. 2010 The First Automated Negotiating Agents Competition (ANAC 2010). In New Trends in Agent‐Based Complex Automated Negotiations, Studies in Computational Intelligence volume 383, ed. Takayuki Ito, Minjie Zhang, Valentin Robu, Shaheen Fatima, and Tokuro Matsuo, 113–135, Berlin: Springer‐Verlag.

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Hao, J., and Leung, H.‐F. 2014. CUHKAgent: An Adaptive Negotiation Strategy for Bilateral Negotiations over Multiple Items. In Novel Insights in Agent‐Based Complex Automated Negotiation, Studies in Computational Intelligence volume 535, ed. I. Marsa‐Maestre, M. A. Lopez‐Carmona, T. Ito, M. Zhang, Q. Bai, and K. Fujita, 71–179. Berlin: Springer.

Google Scholar

[4]

Lin, R., Kraus, S., Baarslag, T., Tykhonov, D., Hindriks, K.; and Jonker, C. M. 2014. Genius: An Integrated Environment for Supporting the Design of Generic Automated Negotiators. Computational Intelligence 30(1): 48–70.

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Baarslag TDastani MSichman JAlechina NDignum V(2024)Multi-deal NegotiationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663255(2668-2673)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663255
Higa RFujita KTakahashi TShimizu TNakadai SWilliams BChen YNeville J(2023)Reward-based negotiating agent strategiesProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i10.26367(11569-11577)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i10.26367
Keskin MÇakan UAydoğan RDignum FLomuscio AEndriss UNowé A(2021)Solver Agent: Towards Emotional and Opponent-Aware Agent for Human-Robot NegotiationProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3463952.3464158(1557-1559)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.5555/3463952.3464158
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AI Magazine Volume 36, Issue 4

Winter 2015

126 pages

ISSN:0738-4602

EISSN:2371-9621

DOI:10.1002/aaai.v36.4

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John Wiley & Sons, Inc.

United States

American Association for Artificial Intelligence

United States

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Published: 01 December 2015

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Baarslag TDastani MSichman JAlechina NDignum V(2024)Multi-deal NegotiationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663255(2668-2673)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663255
Higa RFujita KTakahashi TShimizu TNakadai SWilliams BChen YNeville J(2023)Reward-based negotiating agent strategiesProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i10.26367(11569-11577)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i10.26367
Keskin MÇakan UAydoğan RDignum FLomuscio AEndriss UNowé A(2021)Solver Agent: Towards Emotional and Opponent-Aware Agent for Human-Robot NegotiationProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3463952.3464158(1557-1559)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.5555/3463952.3464158
de Jonge DBistaffa FLevy JDignum FLomuscio AEndriss UNowé A(2021)A Heuristic Algorithm for Multi-Agent Vehicle Routing with Automated NegotiationProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3463952.3464004(404-412)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.5555/3463952.3464004

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Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019

A Compromising Strategy Based on Constraint Relaxation for Automated Negotiating Agents

Automated negotiating agents competition (ANAC)

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