Publications

This page lists papers that cite, use, or mention NegMAS and its ecosystem (including SCML - Supply Chain Management League).

Note

This list is updated periodically. If you have published a paper using NegMAS that is not listed here, please open an issue or submit a pull request.

Last updated: January 2026

How to Cite NegMAS

If you use NegMAS in your research, please cite the following paper:

@inproceedings{mohammad2021negmas,
  title={NegMAS: A Platform for Automated Negotiations},
  author={Mohammad, Yasser and Nakadai, Shinji and Greenwald, Amy},
  booktitle={PRIMA 2020: Principles and Practice of Multi-Agent Systems},
  pages={343--351},
  year={2021},
  publisher={Springer},
  doi={10.1007/978-3-030-69322-0_23}
}

Core NegMAS Papers

These are the main papers describing the NegMAS platform:

• Mohammad, Y., Nakadai, S., Greenwald, A. (2021). NegMAS: A Platform for Automated Negotiations. In: PRIMA 2020: Principles and Practice of Multi-Agent Systems. LNCS, vol 12568. Springer.

• Mohammad, Y., Nakadai, S., Greenwald, A. (2019). NegMAS: A Platform for Situated Negotiations. In: Twelfth International Workshop on Agent-based Complex Automated Negotiations (ACAN 2019).

• Mohammad, Y., Viqueira, E.A., Ayerza, N.A., Greenwald, A., Nakadai, S., Morinaga, S. (2019). Supply Chain Management World: A Benchmark Environment for Situated Negotiations. In: PRIMA 2019. LNCS, vol 11873. Springer. Cited by 38

Competition Papers (ANAC)

Papers describing the Automated Negotiating Agents Competition results using NegMAS/SCML:

• Aydoğan, R., Baarslag, T., Florijn, T.C.P., et al. (2025). The Automated Negotiating Agents Competition (ANAC) 2024 Challenges and Results. In: Proceedings of AAMAS 2025.

• Mohammad, Y., Nakadai, S., Greenwald, A. (2024). Automated Negotiation in Supply Chains: A Generalist Environment for RL/MARL Research. In: PRIMA 2024. LNCS. Springer.

• Aydoğan, R., Baarslag, T., Fujita, K., Hoos, H.H., et al. (2022). The 13th International Automated Negotiating Agent Competition Challenges and Results. In: IJCAI 2022 International Workshops. LNCS, vol 13752. Springer. Cited by 8

• Aydoğan, R., Baarslag, T., Fujita, K., Mell, J., et al. (2020). Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019. In: EUMAS 2020, AT 2020. LNCS, vol 12520. Springer. Cited by 51

Negotiation Strategies & Agents

Papers presenting novel negotiation strategies using NegMAS:

• Miyajima, R., Fujita, K. (2025). CoCoNAT: Combinatorial Concurrent Negotiating Agent Transformer. SSRN Preprint.

• Florijn, T.C.P., Tijdeman, M., Yolum, P., Baarslag, T. (2025). Predicting the Outcome of Ongoing Automated Negotiations. In: PRIMA 2024. LNCS. Springer.

• Aguilera-Luzon, D., de Jonge, D., Larrosa, J. (2025). MiCRO for Multilateral Negotiations. In: PRIMA 2024. LNCS. Springer.

• Chang, S., Fujita, K. (2024). COMB: Scalable Concession-driven Opponent Models Using Bayesian Learning. Group Decision and Negotiation. Cited by 3

• Miyajima, R., Fujita, K. (2024). Deep Reinforcement Learning Framework with Representation Learning for Concurrent Negotiation. In: ICAART 2024.

• Matsuo, H., Fujita, K. (2024). Effective Acceptance Strategy Using Deep Reinforcement Learning. Journal of Information Processing. Cited by 1

• Yoshino, H., Fujita, K. (2024). Clustering-Based Approach to Strategy Selection for Meta-Strategy in Automated Negotiation. In: ICAART 2024.

• Higa, R., Fujita, K., Takahashi, T., Shimizu, T., Morinaga, S. (2023). Reward-based Negotiating Agent Strategies. In: Proceedings of AAAI 2023. Cited by 16

• Chang, S., Fujita, K. (2023). A Scalable Opponent Model Using Bayesian Learning for Automated Bilateral Multi-issue Negotiation. In: Proceedings of AAMAS 2023. Cited by 5

• Chang, S., Fujita, K. (2023). Fine-Tuning Aggregation Convolutional Neural Network Surrogate Model. In: ICAART 2023. Cited by 1

• Hosokawa, Y., Fujita, K. (2023). Multi-Issue Negotiation Protocol with Pre-Domain Narrowing. Applied Sciences. Cited by 3

• Liu, Y., Hadfi, R., Ito, T. (2022). Concession Strategy Adjustment in Automated Negotiation Problems. In: IJCAI 2022 International Workshops. LNCS, vol 13752. Springer.

• Koça, T., Jonker, C.M., Baarslag, T. (2022). Enabling Negotiating Agents to Explore Very Large Outcome Spaces. In: IJCAI 2022 International Workshops. LNCS, vol 13752. Springer. Cited by 1

• Li, Z., Hadfi, R., Ito, T. (2022). Agenda-Based Automated Negotiation Through Utility Decomposition. In: IJCAI 2022 International Workshops. LNCS, vol 13752. Springer. Cited by 1

• Sengupta, A., Nakadai, S., Mohammad, Y. (2022). Transfer Learning Based Adaptive Automated Negotiating Agent Framework. In: Proceedings of IJCAI 2022. Cited by 6

• Ebrahimnezhad, A., Fujita, K. (2022). LuckyAgent2022: A Stop-Learning Multi-Armed Bandit Automated Negotiating Agent. In: ICCAE 2022. Cited by 1

• Sengupta, A., Mohammad, Y., Nakadai, S. (2021). An Autonomous Negotiating Agent Framework with Reinforcement Learning Based Strategies. arXiv:2102.03588. Cited by 48

• Mohammad, Y. (2021). Concurrent Local Negotiations with a Global Utility Function: A Greedy Approach. Autonomous Agents and Multi-Agent Systems, 35, 14. Cited by 14

• Yavuz, C.O.B., Süslü, Ç., Aydogan, R. (2020). Taking Inventory Changes into Account While Negotiating in Supply Chain Management. In: ICAART 2020. Cited by 4

Protocols & Theory

Papers on negotiation protocols and theoretical aspects:

• Mohammad, Y. (2025). Tackling the Protocol Problem in Automated Negotiation. In: Proceedings of AAMAS 2025. Cited by 1

• Florijn, T.C.P., Yolum, P., Baarslag, T. (2025). A Survey on One-to-Many Negotiation: A Taxonomy of Interdependency. In: Proceedings of IJCAI 2025. Cited by 1

• de Jonge, D. (2025). Introduction to Automated Negotiation. arXiv:2511.08659.

• Koça, T., de Jonge, D., Baarslag, T. (2024). Search Algorithms for Automated Negotiation in Large Domains. Annals of Mathematics and Artificial Intelligence. Cited by 3

• Mohammad, Y. (2023). Generalized Bargaining Protocols. In: AI 2023. LNCS, vol 14471. Springer. Cited by 3

• Mohammad, Y. (2023). Optimal Time-based Strategy for Automated Negotiation. Applied Intelligence, 53, 9088-9105. Cited by 9

• Mohammad, Y. (2023). Evaluating Automated Negotiations. In: IEEE WI-IAT 2023. Cited by 2

• Mohammad, Y. (2020). Optimal Deterministic Time-based Policy in Automated Negotiation. In: PRIMA 2020. LNCS, vol 12568. Springer. Cited by 4

• Li, Z., Hadfi, R., Ito, T. (2023). Divide-and-conquer in Automated Negotiations Through Utility Decomposition. IIAI Letters on Informatics and Interdisciplinary Research. Cited by 1

Platforms & Frameworks

Papers describing other platforms that use or compare with NegMAS:

• Ebrahimnezhad, A., Fujita, K. (2023). NegoSim: A Modular and Extendable Automated Negotiation Simulation Platform Considering EUBOA. Applied Sciences. Cited by 7

• Keskin, M.O., Buzcu, B., Koçyiğit, B., et al. (2024). NEGOTIATOR: A Comprehensive Framework for Human-Agent Negotiation. In: Proceedings of AAMAS 2024. Cited by 2

• de Jonge, D. (2025). BINGO: An Algorithm for Automated Negotiations with Hidden Reservation Values. Technical Report.

Applications

Papers applying NegMAS/SCML to real-world problems:

• Arakawa, R., Fujita, K. (2023). Deep Deterministic Policy Gradient for Nested Parallel Negotiation. In: IEEE WI-IAT 2023.

• Rincon, J.A., Costa, A., Julian, V., Carrascosa, C., et al. (2021). A Low-cost Human-Robot Negotiation System. In: PAAMS 2021. LNCS, vol 12946. Springer.

• Hirano, M., Matsushima, H., Izumi, K., Mukai, T. (2020). Simulation of Unintentional Collusion Caused by Auto Pricing in Supply Chain Markets. In: PRIMA 2020. LNCS, vol 12568. Springer. Cited by 3

• Inotsume, H., Aggarwal, A., Higa, R., Nakadai, S. (2020). Path Negotiation for Self-interested Multirobot Vehicles in Shared Space. In: IEEE/RSJ IROS 2020. Cited by 17

• Elakehal, E.E., Vennekens, J. (2021). A Logic-based Multiagent Product Configuration Model. In: IEEE ICICIP 2021.

Theses & Technical Reports

• Aguilera Luzón, D. (2025). New Variants of the MiCRO Negotiation Strategy. Master’s Thesis, Universitat Politècnica de Catalunya.

• Tijdeman, M. (2024). Predicting the Outcome of Automated Negotiations using Machine Learning. Master’s Thesis, Utrecht University.

• Cortés Sancho, F. (2021). Diseño de un Sistema de Negociación Bilateral entre Agentes. Bachelor’s Thesis, Universidad Politécnica de Madrid.

• Brown, I., Zhou, K., Sato, S., et al. (2023). g-RIPS Sendai 2023: SCML Agent Development. Technical Report, Tohoku University.

Books & Book Chapters

• Jonker, C.M., Siebert, L.C., et al. (2024). Reflective Hybrid Intelligence for Meaningful Human Control in Decision-Support Systems. In: Research Handbook on Responsible AI. Edward Elgar Publishing. Cited by 6

• Aydoğan, R., Ito, T., Moustafa, A., Zhang, M. (Eds.) (2021). Recent Advances in Agent-based Negotiation. Studies in Computational Intelligence, vol 958. Springer.

Survey Papers

Papers that survey the field and mention NegMAS:

• Dominguez, R., Cannella, S. (2020). Insights on Multi-agent Systems Applications for Supply Chain Management. Sustainability. Cited by 67

• Fujita, K. (2024). 私のブックマーク: 自動交渉 (My Bookmark: Automated Negotiation). Journal of the Japanese Society for Artificial Intelligence.