Agents with foundation models: advance and vision
Chenghua GONG, Xiang LI
Agents with foundation models: advance and vision
[1] |
Bommasani R, Hudson D A, Adeli E, Altman R, Arora S, ,
|
[2] |
Xi Z, Chen W, Guo X, He W, Ding Y, ,
|
[3] |
Wang L, Ma C, Feng X, Zhang Z, Yang H, Zhang J, Chen Z, Tang J, Chen X, Lin Y, Zhao W X, Wei Z, Wen J . A survey on large language model based autonomous agents. Frontiers of Computer Science, 2024, 18( 6): 186345
|
[4] |
Gronauer S, Diepold K . Multi-agent deep reinforcement learning: a survey. Artificial Intelligence Review, 2022, 55( 2): 895–943
|
[5] |
Qian C, Liu W, Liu H, Chen N, Dang Y, Li J, Yang C, Chen W, Su Y, Cong X, Xu J, Li D, Liu Z, Sun M. ChatDev: communicative agents for software development. 2023, arXiv preprint arXiv: 2307.07924
|
[6] |
Park J S, O’Brien J, Cai C J, Morris M R, Liang P, Bernstein M S. Generative agents: interactive simulacra of human behavior. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 2023, 2
|
[7] |
Chan C M, Chen W, Su Y, Yu J, Xue W, Zhang S, Fu J, Liu Z. ChatEval: towards better LLM-based evaluators through multi-agent debate. In: Proceedings of the 12th International Conference on Learning Representations. 2024
|
[8] |
Ke Z, Liu B. Continual learning of natural language processing tasks: a survey. 2022, arXiv preprint arXiv: 2211.12701
|
[9] |
Wang G, Xie Y, Jiang Y, Mandlekar A, Xiao C, Zhu Y, Fan L, Anandkumar A. Voyager: an open-ended embodied agent with large language models. 2023, arXiv preprint arXiv: 2305.16291
|
[10] |
Yang J C, Dailisan D, Korecki M, Hausladen C I, Helbing D. LLM voting: human choices and AI collective decision making. 2024, arXiv preprint arXiv: 2402.01766
|
/
〈 | 〉 |