Fetching the paper…
Reading the bibliography…
With the explosive influence caused by the success of large language models (LLM) like ChatGPT and GPT-4, there has been an extensive amount of recent work showing that foundation models can be used to solve a large variety of tasks.
Conflict-based search for optimal multi-agent pathfinding
Sharon, G., Stern, R., Felner, A., and Sturtevant, N. R · 2015
Earlier work this paper cites.
Efficient SAT approach to multi-agent path finding under the sum of costs objective
Surynek, P., Felner, A., Stern, R., and Boyarski, E · 2016
Earlier work this paper cites.
Symmetry-breaking constraints for grid-based multi-agent path finding
Li, J., Harabor, D., Stuckey, P. J., Ma, H., and Koenig, S · 2019
Earlier work this paper cites.
PRIMAL: pathfinding via reinforcement and imitation multi-agent learning
Sartoretti, G., Kerr, J., Shi, Y., Wagner, G., Kumar, T. K. S., Koenig, S., and Choset, H · 2019
Earlier work this paper cites.
Multi-agent pathfinding: Definitions, variants, and benchmarks
Stern, R., Sturtevant, N. R., Felner, A., Koenig, S., Ma, H., Walker, T. T., Li, J., Atzmon, D., Cohen, L., Kumar, T. K. S., Barták, R., and Boyarski, E · 2019
Earlier work this paper cites.
DDM: fast near-optimal multi-robot path planning using diversified-path and optimal sub-problem solution database heuristics
Han, S. D. and Yu, J · 2020
Earlier work this paper cites.
Moving agents in formation in congested environments
Li, J., Sun, K., Ma, H., Felner, A., Kumar, T. K. S., and Koenig, S · 2020
Earlier work this paper cites.
Primal$_2$: Pathfinding via reinforcement and imitation multi-agent learning - lifelong
Damani, M., Luo, Z., Wenzel, E., and Sartoretti, G · 2021
Earlier work this paper cites.
Complexity-based prompting for multi-step reasoning
Fu, Y., Peng, H., Sabharwal, A., Clark, P., and Khot, T · 2022
Earlier work this paper cites.
Planning with diffusion for flexible behavior synthesis
Janner, M., Du, Y., Tenenbaum, J. B., and Levine, S · 2022
Earlier work this paper cites.
MAPF-LNS2: fast repairing for multi-agent path finding via large neighborhood search
Li, J., Chen, Z., Harabor, D., Stuckey, P. J., and Koenig, S · 2022
Earlier work this paper cites.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Large language models still can’t plan (A benchmark for llms on planning and reasoning about change)
Valmeekam, K., Hernandez, A. O., Sreedharan, S., and Kambhampati, S · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D · 2022
Cited alongside, same era.
React: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2022
Cited alongside, same era.
Evaluating multi-agent coordination abilities in large language models
Agashe, S., Fan, Y., and Wang, X. E · 2023
Demystifying gpt self-repair for code generation
Olausson, T. X., Inala, J. P., Wang, C., Gao, J., and Solar-Lezama, A · 2023
Later among the works it cites.
Dialogue games for benchmarking language understanding: Motivation, taxonomy, strategy
Schlangen, D · 2023
Later among the works it cites.
Reflexion: an autonomous agent with dynamic memory and self-reflection
Shinn, N., Labash, B., and Gopinath, A · 2023
Later among the works it cites.
Automatic prompt augmentation and selection with chain-of-thought from labeled data
Shum, K., Diao, S., and Zhang, T · 2023
Later among the works it cites.
Learn to follow: Decentralized lifelong multi-agent pathfinding via planning and learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Improving factuality and reasoning in language models through multiagent debate
Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., and Mordatch, I · 2023
Cited alongside, same era.
The capacity for moral self-correction in large language models
Ganguli, D., Askell, A., Schiefer, N., Liao, T., Lukošiūtė, K., Chen, A., Goldie, A., Mirhoseini, A., Olsson, C., Hernandez, D., et al · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., and Stoica, I · 2023
Cited alongside, same era.
Theory of mind for multi-agent collaboration via large language models
Li, H., Chong, Y. Q., Stepputtis, S., Campbell, J., Hughes, D. T., Lewis, C., and Sycara, K. P · 2023
Cited alongside, same era.
Roco: Dialectic multi-robot collaboration with large language models
Mandi, Z., Jain, S., and Song, S · 2023
Cited alongside, same era.
A language agent for autonomous driving
Mao, J., Ye, J., Qian, Y., Pavone, M., and Wang, Y · 2023
Cited alongside, same era.
Teaching large language models to self-debug
Chen, X., Lin, M., Schärli, N., and Zhou, D
Cited in the paper.
Skrynnik, A., Andreychuk, A., Nesterova, M., Yakovlev, K., and Panov, A · 2023
Later among the works it cites.
Language conditioned traffic generation
Tan, S., Ivanovic, B., Weng, X., Pavone, M., and Kraehenbuehl, P · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Canton-Ferrer, C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P. S., Lachaux, M., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Subramanian, R., Tan, X. E., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., and Scialom, T · 2023
Later among the works it cites.
On the planning abilities of large language models–a critical investigation
Valmeekam, K., Marquez, M., Sreedharan, S., and Kambhampati, S · 2023
Later among the works it cites.
Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2023
Later among the works it cites.
In-context instruction learning
Ye, S., Hwang, H., Yang, S., Yun, H., Kim, Y., and Seo, M · 2023
Later among the works it cites.
Building cooperative embodied agents modularly with large language models
Zhang, H., Du, W., Shan, J., Zhou, Q., Du, Y., Tenenbaum, J. B., Shu, T., and Gan, C · 2023
Later among the works it cites.