Hua, W., Liu, O., Li, L., Amayuelas, A., Chen, J., Jiang, L., ... & Zhang, Y. (2023). Game-theoretic LLM: Agent Workflow for Negotiation Games . [Unpublished manuscript ] . Rutgers University.

Luo, J., Zhang, W., Su, J., et al. (2024). Research progress of multi-agent learning in games. Systems Engineering and Electronics , 46 (5), 1628-1655.

Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., ... & Simonyan, K. (2022). Flamingo: A visual language model for few-shot learning. Advances in Neural Information Processing Systems , 35 , 23716-23736.

Schultz, T. R., & Kestenbaum, N. R. (1993). Learning causal patterns: Making a transition from data-driven to theory-driven learning. Machine Learning , 11 (2-3), 173-194.https://doi.org/10.1023/A:1022607123329

Silver, D., Hubert, T., Schrittwieser, J., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science , 362 (6419), 1140-1144.https://doi.org/10.1126/science.aar6404

Huang, M., Tang, J., et al. (2023). SPaR: Self-play with tree-search refinement to improve instruction-following in large language models. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics , 10289-10305.

Phelps, S., Russell, Y., et al. (2023). Investigating emergent goal-like behaviour in large language models using experimental economics. arXiv preprint . https://arxiv.org/abs/2305.07970

Hua, W., et al. (2023). Supplementary materials for: Game-theoretic LLM: Agent Workflow for Negotiation Games . [Dataset ] . Rutgers University.