The DOI matches its Crossref record; 167 of the 178 references it cites were found in a registry or an index.
Multiagent Systems: A Survey from a Machine Learning Perspective
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“Compared to a single-agent setting, an MAS more realistically represents complex real-world scenarios involving multiple decision-makers or information sources [7,8].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [8]
Citations
Cites
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
Smith (1980) · IEEE Transactions on Computers · doi:10.1109/tc.1980.1675516 · Cited by 3,725
Markov games as a framework for multi-agent reinforcement learning
Littman, M. L. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-335-6.50027-1 · Cited by 2,789
Shoham, Y. (1992) · Artificial Intelligence · doi:10.1016/0004-3702(93)90034-9 · Cited by 2,658
BDI Agents: From Theory to Practice
Rao, A. S., & Georgeff, M. (1995) · Cited by 2,577
Seel, P. D. N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5794 · Cited by 2,566
Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents
Tan, M. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50049-6 · Cited by 1,900
Cooperative Mobile Robotics: Antecedents and Directions
Cao, Y. U., Fukunaga, A. S., & Kahng, A. B. (1997) · Autonomous Robots · doi:10.1023/a:1008855018923 · Cited by 1,240
Ridley, M. (1996) · Cited by 981
Flagged first, then by citations
11 of the 178 references couldn’t be matched to a known paper.
Research works printed but not linked: 26
- AAAI. 1995. Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), Menlo Park, CA, AAAI Press. Victor Lessor-General Chair.
- Asada, M. and Kitano, H. (Eds.) 1999. RoboCup-98: Robot Soccer World Cup II, Springer Verlag: Berlin. Lecture Notes in Artificial Intelligence, vol. 1604.
- Bowling, M. and Veloso, M. 1999. Motion control in dynamic multirobot environments. In Proceedings of The 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA'99), Monterrey.
- Finin, T., McKay, D., Fritzson, R., and McEntire, R. 1994. KQML: An information and knowledge exchange protocol. In Knowledge Building and Knowledge Sharing, K. Fuchi and T. Yokoi (Eds.), Ohmsha and IOS Press.
- Gutmann, J.-S., Hattzack, W., Herrmann, I., Nebel, B., Rittinger, F., Topor, A., Weigel, T., and Welsch, B. 1998. The CS Freiburg team. In Proceedings of the Second RoboCupWorkshop, M. Asada (Ed.), Paris, France, pp. 451–458.
- Han, W.-G., Baek, S.-M., Kuc, T.-Y., and Kwan, S.K. 1996. Path planning of visual-served multiple mobile robots using the genetic algorithms. In Proceedings of the Micro-Robot World Cup Soccer Tournament, Taejon, Korea, IEEE Robotics and Automation Society, pp. 57–63.
- Holland, O.E. 1996. Multiagent systems: Lessons from social insects and collective robotics. In Adaptation, Coevolution and Learning in Multiagent Systems: Papers fromthe 1996 AAAI Spring Symposium, Menlo Park, CA, AAAI Press, pp. 57–62. AAAI Technical Report SS-96-01.
- Hong, S.-G., Eom, T.-D., Jeong, Il.-K., Choi, C., Shin, J.-H. and Lee, J.-J. 1996. Development of soccer-playing robots: Control, cooperation, and strategy. In Proceedings of the Micro-Robot World Cup Soccer Tournament, Taejon, Korea, IEEE Robotics and Automation Society, pp. 134–141.
- Inoue, N. and Wilkin, S. 1997. Learning system for the formation and the pass-play. Presented at the Poster Session of the IROS-96 Workshop on RoboCup.
- Kim, J.-H. (Ed.). 1996. Proceedings of the Micro-Robot World Cup Soccer Tournament, Taejon, Korea.
- Kim, D.-Y. and Chung, M.J. 1996. Path planning for multi-mobile robots in the dynamic environment. In Proceedings of the Micro-Robot World Cup Soccer Tournament, Taejon, Korea, IEEE Robotics and Automation Society, pp. 127–132.
- Kitano, H. (Ed.). 1996. Proceedings of the IROS-96 Workshop on RoboCup, Osaka, Japan.
- Kitano, H. (Ed.). 1998. RoboCup-97: Robot Soccer World Cup I, Springer Verlag: Berlin.
- Korf, R.E. 1992. A simple solution to pursuit games. In Working Papers of the 11th InternationalWorkshop on Distributed Artificial Intelligence, pp. 183–194.
- Koza, J.R. 1992. Genetic Programming, MIT Press.
- Matsuyama, T. 1997. Cooperative distributed vision. In Proceedings of the First International Workshop on Cooperative Distributed Vision, Kyoto, Japan.
- Mizuno, H., Kourogi, M., and Muraoka, Y. 1996. Building shoobot possible to dribble and shoot. In Proceedings of the IROS-96Workshop on RoboCup.
- Sen, S. (Ed.). 1996. Adaptation, Coevolution and Learning in Multiagent Systems: Papers from the 1996 AAAI Spring Symposium, Menlo Park, CA, AAAI, AAAI Press. AAAI Technical Report SS-96-01.
- Shim, H.-S., Jung, M.-J., Kim, H.-S., Choi, I.-H., Han, W.-S., and Kim, J.-H. 1996. Designing distributed control architecture for cooperative multiagent systems. In Proceedings of the Micro-Robot World Cup Soccer Tournament, Taejon, Korea, IEEE Robotics and Automation Society, pp. 19–25.
- Stone, P. and Veloso, M. 1996. Using machine learning in the soccer server. In Proceedings of the IROS-96 Workshop on RoboCups, Osaka, Japan, pp. 19–27.
- Sugawara, T. and Lesser, V. 1995. Learning coordination plans in distributed OS environments. In Proceedings of the First International Conference on Multi-Agent Systems, V. Lesser (Ed.), San Francisco, CA, MIT Press, p. 462.
- Tambe, M. 1996. Tracking dynamic team activitys. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, Menlo Park, California, AAAI Press.
- Uther, W.T.B. and Veloso, M.M. 1997. Generalizing adversarial reinforcement learning. In Proceedings of the AAAI Fall Symposium on Model Directed Autonomous Systems.
- Veloso, M., Pagello, E., and Kitano, H. (Eds.). 2000. RoboCup-99: Robot Soccer World Cup III. Springer Verlag: Berlin.
- Weiß, G. 1996. ECAI-96 Workshop on Learning in Distributed Arti-ficial Intelligence, Call for Papers.
- Weiß, G. and Sen, S. (Eds.)(1996). Adaptation and Learning in Multiagent Systems, Springer Verlag: Berlin.
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Learning and Vision-Based Obstacle Avoidance and Navigation
Tian, J., & Tang, Y. (2010) · Advanced information and knowledge processing · doi:10.1007/978-1-84996-329-9_7
Metadata
- Authors
- Stone, P., & Veloso, M.
- Affiliations
- AT&T (United States) (US)
- Carnegie Mellon University (US)
- Venue
- Autonomous Robots
- Published
- 2000-06
- Volume
- 8
- Issue
- 3
- Pages
- 345-383
- ISSN
- 0929-5593 (ISSN-L)
- 1573-7527
- OpenAlex
- W2147492008
- Cited by
- 1,218
- References
- 178
- Citations by year
- 2026: 57 · 2025: 42 · 2024: 31 · 2023: 21 · 2022: 37 · 2021: 59 · 2020: 65 · 2019: 58 · 2018: 64 · 2017: 69 · 2016: 42 · 2015: 48 · 2014: 52 · 2013: 45 · 2012: 42
- FWCI
- 32.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Autonomous Robots (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Keywords
- multi-agent systems, distributed artificial intelligence, distributed problem solving, behavior management, robot soccer
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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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