Fetching the paper…
Reading the bibliography…
Social norm is defined as a shared standard of acceptable behavior in a society.
M. L. Littma, “Markov games as a framework for multi-agent reinforcement learning,” ICML , pp. 157––163, 1994
1994
Earlier work this paper cites.
J. Delgado, “Emergence of social conventions in complex networks,” Artificial Intelligence , vol. 141, pp. 171–185, 10 2002
2002
Earlier work this paper cites.
S. Sen and S. Airiau, “Emergence of norms through social learning,” in Proceedings of the 20th International Joint Conference on Artifical Intelligence , ser. IJCAI’07. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc, 2007, p. 1507–1512
2007
Earlier work this paper cites.
D. Villatoro, J. Sabater-Mir, and S. Sen, “Social instruments for robust convention emergence,” in IJCAI International Joint Conference on Artificial Intelligence , 01 2011, pp. 420–425
2011
Earlier work this paper cites.
C. Yu, M. Zhang, F. Ren, and X. Luo, “Emergence of social norms through collective learning in networked agent societies,” in Proceedings of the 2013 International Conference on Autonomous Agents and Multi-Agent Systems , ser. AAMAS ’13. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2013, p. 475–482
2013
Earlier work this paper cites.
H. Franks, N. Griffiths, and A. Jhumka, “Manipulating convention emergence using influencer agents,” Autonomous Agents and Multi-Agent Systems , vol. 26, 05 2013
2013
Earlier work this paper cites.
K. Nyborg, J. M. Anderies, A. Dannenberg, T. Lindahl, C. Schill, M. Schlüter, W. N. Adger, K. J. Arrow, S. Barrett, S. Carpenter et al. , “Social norms as solutions,” Science , vol. 354, no. 6308, pp. 42–43, 2016
2016
Earlier work this paper cites.
A. Lerer and A. Peysakhovich, “Learning existing social conventions via observationally augmented self-play,” in Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , ser. AIES ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 107–114. [Online]. Available: https://doi.org/10.1145/3306618.3314268
2019
Earlier work this paper cites.
R. Köster, K. McKee, R. Everett, L. Weidinger, W. Isaac, E. Hughes, E. Duenez-Guzman, T. Graepel, M. Botvinick, and J. Leibo, Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences , 10 2020
2020
Earlier work this paper cites.
2022
Earlier work this paper cites.
L. Ouyang, J. Wu, and X. Jiang, “Training language models to follow instructions with human feedback,” NeurIPS , 2022
2022
Earlier work this paper cites.
X. Chen, Z. Li, and X. Di, “Social learning in markov games: Empowering autonomous driving,” in 2022 IEEE Intelligent Vehicles Symposium (IV) , 2022, pp. 478–483
2022
Earlier work this paper cites.
X. Chen, X. Di, and Z. Li, “Social learning for sequential driving dilemmas,” Games , vol. 14, no. 3, 2023
2023
Earlier work this paper cites.
M. Kwon, S. M. Xie, K. Bullard, and D. Sadigh, “Reward design with language models,” in The Eleventh International Conference on Learning Representations , 2023
2023
Earlier work this paper cites.
S. Mao, Y. Cai, Y. Xia, W. Wu, X. Wang, F. Wang, T. Ge, and F. Wei, “Alympics: Language agents meet game theory,” 2023
2023
Cited alongside, same era.
J. Horton, “Large language models as simulated economic agents: What can we learn from homo silicus?” SSRN Electronic Journal , 01 2023
2023
Cited alongside, same era.
E. Akata, L. Schulz, J. Coda-Forno, S. J. Oh, M. Bethge, and E. Schulz, “Playing repeated games with large language models,” 2023
2023
Cited alongside, same era.
J. Brand, A. Israeli, and D. Ngwe, “Using gpt for market research,” SSRN Electronic Journal , 2023
2023
Cited alongside, same era.
Y. Chen, T. X. Liu, Y. Shan, and S. Zhong, “The emergence of economic rationality of gpt,” Proceedings of the National Academy of Sciences , vol. 120, no. 51, p. e2316205120, 2023
2023
Cited alongside, same era.
H. Mohapatra and S. R. Mishra, “Exploring ai tool’s versatile responses: An in-depth analysis across different industries and its performance evaluation,” 2023
2023
Later among the works it cites.
K. Ruan, X. He, J. Wang, X. Zhou, H. Feng, and A. Kebarighotbi, “S2e: Towards an end-to-end entity resolution solution from acoustic signal,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2024, pp. 10 441–10 445
2024
Closest in time.
2024
Closest in time.
Y. Fu, Y. Li, and X. Di, “Gendds: Generating diverse driving video scenarios with prompt-to-video generative model,” in 2024 IEEE International Conference on Intelligent Transportation Systems (ITSC) , 2024
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Dillion, N. Tandon, Y. Gu, and K. Gray, “Can ai language models replace human participants?” Trends in Cognitive Sciences , vol. 27, no. 7, pp. 597–600, 2023
2023
Cited alongside, same era.
G. V. Aher, R. I. Arriaga, and A. T. Kalai, “Using large language models to simulate multiple humans and replicate human subject studies,” in Proceedings of the 40th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 202. PMLR, 23–29 Jul 2023, pp. 337–371
2023
Cited alongside, same era.
L. Argyle, E. Busby, N. Fulda, J. Gubler, C. Rytting, and D. Wingate, “Out of one, many: Using language models to simulate human samples,” Political Analysis , vol. 31, pp. 1–15, 02 2023
2023
Cited alongside, same era.
Y. Xu, S. Wang, P. Li, F. Luo, X. Wang, W. Liu, and Y. Liu, “Exploring large language models for communication games: An empirical study on werewolf,” 2023
2023
Cited alongside, same era.
C. Fan, J. Chen, Y. Jin, and H. He, “Can large language models serve as rational players in game theory? a systematic analysis,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” 2023
2023
Cited alongside, same era.
2024
Closest in time.
2024
Closest in time.
K. Ruan, X. Wang, and X. Di, “From twitter to reasoner: Understand mobility travel modes and sentiment using large language models,” in 2024 IEEE International Conference on Intelligent Transportation Systems (ITSC) , 2024
2024
Closest in time.
M. Chahine, T.-H. Wang, H. Zhang, W. Xiao, D. Rus, and C. Gan, “Large language models can design game-theoretic objectives for multi-agent planning,” 2024. [Online]. Available: https://openreview.net/forum?id=DnkCvB8iXR
2024
Closest in time.
T. R. Sumers, S. Yao, K. Narasimhan, and T. L. Griffiths, “Cognitive architectures for language agents,” 2024
2024
Closest in time.
C. Peng, D. Zhang, and U. Mitra, “Graph identification and upper confidence evaluation for causal bandits with linear models,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2024, pp. 7165–7169
2024
Closest in time.
OpenAI, J. Achiam, and et al., “GPT-4 Technical Report,” 2024
2024
Closest in time.
OpenAI. (2024) OpenAI API Reference. [Online]. Available: https://platform.openai.com/docs/api-reference/chat
2024
Closest in time.
——. (2024) OpenAI Tokenizer. [Online]. Available: https://platform.openai.com/tokenizer
2024
Closest in time.