Multi-Agent Systems and AI
Bibliographic References tagged with Multi-Agent Systems and AI
Not finding what you're looking for? Try using Advanced Search.
Not finding what you're looking for? Try using Advanced Search.
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.
X. Wang et al.,
“Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study.”, in Proc. of the International World Wide Web Conference (WWW ’23), 2023, pp. 3592–3602.
X. Wang et al.,
“Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study.”, in Proc. of the International World Wide Web Conference (WWW ’23), 2023, pp. 3592–3602.
P. Tylkin, G. Radanovic, and D. C. Parkes,
“Learning Robust Helpful Behaviors in Two-Player Cooperative Atari Environments”, in Proc. 20th Int. Conf. on Auton. Agents and Multiagent Systems (AAMAS), 2021, pp. 1686–1688.
P. Tylkin, G. Radanovic, and D. C. Parkes,
“Learning Robust Helpful Behaviors in Two-Player Cooperative Atari Environments”, in Proc. 20th Int. Conf. on Auton. Agents and Multiagent Systems (AAMAS), 2021, pp. 1686–1688.
S. A. Wu, R. E. Wang, J. A. Evans, J. Tenenbaum, D. C. Parkes, and M. Kleiman-Weiner,
“Too many cooks: Coordinating multi-agent collaboration through inverse planning”, in Proc. 42nd Annual Meeting of the Cognitive Science Society, 2020, pp. 889–895.
S. A. Wu, R. E. Wang, J. A. Evans, J. Tenenbaum, D. C. Parkes, and M. Kleiman-Weiner,
“Too many cooks: Coordinating multi-agent collaboration through inverse planning”, in Proc. 42nd Annual Meeting of the Cognitive Science Society, 2020, pp. 889–895.
D. Parkes,
“Playing with symmetry with neural networks”, Nature Machine Intelligence , vol. 3, no. 8, pp. 658–658, 2021.
D. Parkes,
“Playing with symmetry with neural networks”, Nature Machine Intelligence , vol. 3, no. 8, pp. 658–658, 2021.