Autonomous agents modelling other agents: A comprehensive survey and open problems
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None of the previous surveys of related RL subfields have covered zero-shot generalisation in RL explicitly.
“While there have been previous surveys of related subfields in RL, none have covered zero-shot generalisation in RL explicitly.”
From A Survey of Zero-shot Generalisation in Deep Reinforcement Learning (2023) · cites this paper as Albrecht and Stone (2018)
Modelling other agents can be seen as a form of generalisation problem (even in single-agent RL), if the environment contains a distribution over agents.
“Albrecht and Stone (2018) survey methods for modelling other agents, which can be seen as a form of generalisation problem (even in single-agent RL), if the environment contains a distribution over agents.”
From A Survey of Zero-shot Generalisation in Deep Reinforcement Learning (2023) · cites this paper as Albrecht and Stone (2018)
Cites
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STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving
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Circumscription—A form of non-monotonic reasoning
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Cooperative Multi-Agent Learning: The State of the Art
Panait, L., & Luke, S. (2005) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-005-2631-2 · Cited by 1,237 · see citing works
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Deep Reinforcement Learning: An Overview
Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762 · see citing works
A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems
Silva, F. L. D., & Costa, A. H. R. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11396 · Cited by 273 · see citing works
He, J., Treude, C., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712003 · Cited by 246 · see citing works
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
Kirk, R., Zhang, A., Grefenstette, E., & Rocktäschel, T. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14174 · Cited by 193 · see citing works
“Albrecht and Stone (2018) survey methods for modelling other agents, which can be seen as a form of generalisation problem (even in single-agent RL), if the environment contains a distribution over agents.”
BackgroundOpen Problems in Cooperative AI
Dafoe, A., Hughes, E., Bachrach, Y., Collins, T., McKee, K. R., Leibo, J. Z., Larson, K., & Graepel, T. (2020) · arXiv · doi:10.48550/arxiv.2012.08630 · Cited by 93 · see citing works
Multi-agent modeling and simulation in the AI age
Fan, W., Chen, P., Shi, D., Guo, X., & Kou, L. (2021) · Tsinghua Science and Technology · doi:10.26599/tst.2021.9010005 · Cited by 50 · see citing works
He, G., Demartini, G., & Gadiraju, U. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713218 · Cited by 23 · see citing works
Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
Hanna, J. P., Rahman, A., Fosong, E., Eiras, F., Dobre, M., Redford, J., Ramamoorthy, S., & Albrecht, S. V. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635903 · Cited by 21 · see citing works
Self-citationA Survey of Context Engineering for Large Language Models
Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.13334 · Cited by 13 · see citing works
Agent EXPRI: Licence to Explain
Mosca, F., Sarkadi, Ş., Such, J. M., & McBurney, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-51924-7_2 · Cited by 11 · see citing works
Multi-Agent Risks from Advanced AI
Hammond, L., Chan, A., Clifton, J., Hoelscher-Obermaier, J., Khan, A., McLean, E., Smith, C., Barfuss, W., Foerster, J., Gavenčiak, T., et al. (2025) · arXiv · doi:10.48550/arxiv.2502.14143 · Cited by 10 · see citing works
Interpretable Goal-based Prediction and Planning for Autonomous Driving
Albrecht, S. V., Brewitt, C., Wilhelm, J., Gyevnar, B., Eiras, F., Dobre, M., & Ramamoorthy, S. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9560849 · Cited by 12 · see citing works
Self-citation
- Authors
- Albrecht, S. V., & Stone, P.
- Affiliations
- University of Edinburgh (GB)
- The University of Texas at Austin (US)
- Corresponding author
- Albrecht, Stefano V.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 258
- Pages
- 66-95
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2758442112
- Cited by
- 417
- References
- 5
- Citations by year
- 2026: 33 · 2025: 42 · 2024: 37 · 2023: 50 · 2022: 56 · 2021: 71 · 2020: 62 · 2019: 52 · 2018: 13 · 2017: 1
- FWCI
- 27.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Raytheon Company
- Alexander von Humboldt-Stiftung
- National Science Foundation (IIS-1637736, IIS-1651089, IIS-1724157)
- Intel Corporation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- autonomous agents, multi-agent systems, agent-based modeling, goal modeling, action prediction, open problems
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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