Leibo, Joel Z.

Melting Pot 2.0

Agapiou, J. P., Vezhnevets, A. S., Duéñez-Guzmán, E. A., Matyas, J., Mao, Y., Sunehag, P., Köster, R., Madhushani, U., Kopparapu, K., Comanescu, R., et al. (2022) · arXiv

Multi-Agent Systems and NegotiationReinforcement Learning in Robotics

Cited by 3

A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings

Vinitsky, E., Köster, R., Agapiou, J. P., Duéñez-Guzmán, E., Vezhnevets, A. S., & Leibo, J. Z. (2021) · arXiv

Multi-Agent Systems and NegotiationReinforcement Learning in Robotics

Cited by 2

Generative AI collective behavior needs an interactionist paradigm

Ferrarotti, L., Campedelli, G. M., Dessì, R., Baronchelli, A., Iacca, G., Carley, K. M., Pentland, A., Leibo, J. Z., Evans, J., & Lepri, B. (2026) · arXiv

Ethics and Social Impacts of AIMulti-Agent Systems and Negotiation

Cited by 1

A theory of appropriateness with applications to generative artificial intelligence

Leibo, J. Z., Vezhnevets, A. S., Diaz, M., Agapiou, J. P., Cunningham, W. A., Sunehag, P., Haas, J., Koster, R., Duéñez-Guzmán, E. A., Isaac, W. S., et al. (2024) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 1