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Large language models (LLMs) as autonomous agents offer a novel avenue for tackling real-world challenges through a knowledge-driven manner.
D. M. Hawkins, “The problem of overfitting,” Journal of chemical information and computer sciences , vol. 44, no. 1, pp. 1–12, 2004
2004
Earlier work this paper cites.
A. Tampuu, T. Matiisen, D. Kodelja, I. Kuzovkin, K. Korjus, J. Aru, J. Aru, and R. Vicente, “Multiagent cooperation and competition with deep reinforcement learning,” PloS one , vol. 12, no. 4, p. e0172395, 2017
2017
Earlier work this paper cites.
J. K. Gupta, M. Egorov, and M. Kochenderfer, “Cooperative multi-agent control using deep reinforcement learning,” in Autonomous Agents and Multiagent Systems: AAMAS 2017 Workshops, Best Papers, São Paulo, Brazil, May 8-12, 2017, Revised Selected Papers 16 . Springer, 2017, pp. 66–83
2017
Earlier work this paper cites.
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal, “Explaining explanations: An overview of interpretability of machine learning,” in IEEE 5th International Conference on data science and advanced analytics . IEEE, 2018, pp. 80–89
2018
Earlier work this paper cites.
M. M. Morando, Q. Tian, L. T. Truong, and H. L. Vu, “Studying the safety impact of autonomous vehicles using simulation-based surrogate safety measures,” Journal of advanced transportation , vol. 2018, no. 1, p. 6135183, 2018
2018
Earlier work this paper cites.
E. Leurent et al. , “An environment for autonomous driving decision-making,” 2018
2018
Earlier work this paper cites.
X. Ying, “An overview of overfitting and its solutions,” in Journal of physics: Conference series , vol. 1168. IOP Publishing, 2019, p. 022022
2019
Earlier work this paper cites.
A. Papadoulis, M. Quddus, and M. Imprialou, “Evaluating the safety impact of connected and autonomous vehicles on motorways,” Accident Analysis & Prevention , vol. 124, pp. 12–22, 2019
2019
Earlier work this paper cites.
N. Virdi, H. Grzybowska, S. T. Waller, and V. Dixit, “A safety assessment of mixed fleets with connected and autonomous vehicles using the surrogate safety assessment module,” Accident Analysis & Prevention , vol. 131, pp. 95–111, 2019
2019
Earlier work this paper cites.
A. Amini, I. Gilitschenski, J. Phillips, J. Moseyko, R. Banerjee, S. Karaman, and D. Rus, “Learning robust control policies for end-to-end autonomous driving from data-driven simulation,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 1143–1150, 2020
2020
Earlier work this paper cites.
K. Xu, X. Xiao, J. Miao, and Q. Luo, “Data driven prediction architecture for autonomous driving and its application on apollo platform,” in IEEE Intelligent Vehicles Symposium . IEEE, 2020, pp. 175–181
2020
Earlier work this paper cites.
N. Jaipuria, X. Zhang, R. Bhasin, M. Arafa, P. Chakravarty, S. Shrivastava, S. Manglani, and V. N. Murali, “Deflating dataset bias using synthetic data augmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 772–773
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
D. Bogdoll, J. Breitenstein, F. Heidecker, M. Bieshaar, B. Sick, T. Fingscheidt, and M. Zöllner, “Description of corner cases in automated driving: Goals and challenges,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1023–1028
2021
Earlier work this paper cites.
Y. Zhang, P. Tiňo, A. Leonardis, and K. Tang, “A survey on neural network interpretability,” IEEE Transactions on Emerging Topics in Computational Intelligence , vol. 5, no. 5, pp. 726–742, 2021
2021
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Cited alongside, same era.
L. Chen, Y. Li, C. Huang, Y. Xing, D. Tian, L. Li, Z. Hu, S. Teng, C. Lv, J. Wang et al. , “Milestones in autonomous driving and intelligent vehicles—part 1: Control, computing system design, communication, hd map, testing, and human behaviors,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2023
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Chen, Y. Su, J. Zuo, C. Yang, C. Yuan, C.-M. Chan, H. Yu, Y. Lu, Y.-H. Hung, C. Qian et al. , “Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors,” in The Twelfth International Conference on Learning Representations , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2998–3009
2023
Cited alongside, same era.
J. Mai, J. Chen, G. Qian, M. Elhoseiny, B. Ghanem et al. , “Llm as a robotic brain: Unifying egocentric memory and control,” 2023
2023
Cited alongside, same era.
J. Pan, “What in-context learning “learns” in-context: Disentangling task recognition and task learning,” Ph.D. dissertation, Princeton University, 2023
2023
Cited alongside, same era.
S. Wang, Y. Zhu, Z. Li, Y. Wang, L. Li, and Z. He, “Chatgpt as your vehicle co-pilot: An initial attempt,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Q. Lyu, S. Havaldar, A. Stein, L. Zhang, D. Rao, E. Wong, M. Apidianaki, and C. Callison-Burch, “Faithful chain-of-thought reasoning,” in The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL 2023) , 2023
2023
Later among the works it cites.
D. Chen, M. R. Hajidavalloo, Z. Li, K. Chen, Y. Wang, L. Jiang, and Y. Wang, “Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic,” IEEE Transactions on Intelligent Transportation Systems , vol. 24, no. 11, pp. 11 623–11 638, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Cui, Y. Ma, X. Cao, W. Ye, and Z. Wang, “Drive as you speak: Enabling human-like interaction with large language models in autonomous vehicles,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 902–909
2024
Closest in time.
C. Cui, Y. Ma, X. Cao, W. Ye, and Z. Wang, “Receive, reason, and react: Drive as you say, with large language models in autonomous vehicles,” IEEE Intelligent Transportation Systems Magazine , 2024
2024
Closest in time.
Y. Shen, K. Song, X. Tan, D. Li, W. Lu, and Y. Zhuang, “Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
R. Gong, Q. Huang, X. Ma, Y. Noda, Z. Durante, Z. Zheng, D. Terzopoulos, L. Fei-Fei, J. Gao, and H. Vo, “Mindagent: Emergent gaming interaction,” in Findings of the Association for Computational Linguistics: NAACL 2024 , 2024, pp. 3154–3183
2024
Closest in time.
H. Shao, Y. Hu, L. Wang, G. Song, S. L. Waslander, Y. Liu, and H. Li, “Lmdrive: Closed-loop end-to-end driving with large language models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 120–15 130
2024
Closest in time.
M. L. Team. (2024) Introducing meta llama 3: The most capable openly available llm to date. [Online]. Available: https://ai.meta.com/blog/meta-llama-3/
2024
Closest in time.