Multi-Agent Systems and AI
Bibliographic References tagged with Multi-Agent Systems and AI
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M. Bichler and D. C. Parkes,
“Differentiable economics: Strategic behavior, mechanisms, and machine learning”, Comm. ACM, vol. 68(9):80–88, 2025.
M. Bichler and D. C. Parkes,
“Differentiable economics: Strategic behavior, mechanisms, and machine learning”, Comm. ACM, vol. 68(9):80–88, 2025.
S. Fish et al.,
“Generative social choice”, Journal of the ACM, vol. 73 (2), no. 1-11, 2026.
S. Fish et al.,
“Generative social choice”, Journal of the ACM, vol. 73 (2), no. 1-11, 2026.
G. Personnat, T. Lin, S. Hossain, and D. C. Parkes,
“Learning to play multi-follower Bayesian Stackelberg games”, In Proc. 14th Int Conf on Learning Representations (ICLR), vol. 105160-105189. 2026.
G. Personnat, T. Lin, S. Hossain, and D. C. Parkes,
“Learning to play multi-follower Bayesian Stackelberg games”, In Proc. 14th Int Conf on Learning Representations (ICLR), vol. 105160-105189. 2026.
Y. Jiang, D. C. Parkes, and T. Wang,
“Duality for optimal multi-item, multi-bidder auction design: Revenue certificates through deep learning”, Proc. 27th ACM Conf on Economics and Computation (EC’26), 2026.
Y. Jiang, D. C. Parkes, and T. Wang,
“Duality for optimal multi-item, multi-bidder auction design: Revenue certificates through deep learning”, Proc. 27th ACM Conf on Economics and Computation (EC’26), 2026.
R. Trivedi, K. Sharma, and D. C. Parkes,
“Inner speech as behavior guides: Steerable imitation of diverse behaviors for Human-AI coordination”, Proc. 39th Annual Conference on Neural Information Processing Systems NeurIPS. 2025.
R. Trivedi, K. Sharma, and D. C. Parkes,
“Inner speech as behavior guides: Steerable imitation of diverse behaviors for Human-AI coordination”, Proc. 39th Annual Conference on Neural Information Processing Systems NeurIPS. 2025.
T. Wang, H. Dong, Y. Jiang, D. C. Parkes, and M. Tambe,
“On diffusion models for multi-agent partial observability: Shared attractors, error bounds, and composite flow”, Proc. 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025. pp. 2143–2152, 2025.
T. Wang, H. Dong, Y. Jiang, D. C. Parkes, and M. Tambe,
“On diffusion models for multi-agent partial observability: Shared attractors, error bounds, and composite flow”, Proc. 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025. pp. 2143–2152, 2025.
L. Hammond et al.,
“Multi-Agent Risks from Advanced AI”. 2025.
L. Hammond et al.,
“Multi-Agent Risks from Advanced AI”. 2025.
M. H. Tessler et al.,
“AI can help humans find common ground in democratic deliberation”, Science, vol. 386, no. 6719, 2024.
M. H. Tessler et al.,
“AI can help humans find common ground in democratic deliberation”, Science, vol. 386, no. 6719, 2024.
E. Zhang et al.,
“Position: Social Environment Design Should be Further Developed for AI-based Policy-Making”, in Proc. 41st International Conference on Machine Learning, ICML 2024, 2024.
E. Zhang et al.,
“Position: Social Environment Design Should be Further Developed for AI-based Policy-Making”, in Proc. 41st International Conference on Machine Learning, ICML 2024, 2024.
M. Finkelstein et al.,
“Explainable Reinforcement Learning via Model Transforms”, in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2022.
M. Finkelstein et al.,
“Explainable Reinforcement Learning via Model Transforms”, in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2022.