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Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Abstract
Multiagent systems combine multiple autonomous entities, each having diverging interests or different information. This overview of the field offers a computer science perspective, but also draws on ideas from game theory, economics, operations research, logic, philosophy and linguistics. It will serve as a reference for researchers in each of these fields, and be used as a text for advanced undergraduate or graduate courses. The authors emphasize foundations to create a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, game theory, multiagent communication and learning, social choice, mechanism design, auctions, cooperative game theory, and modal logics of knowledge and belief. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes and mathematical programming.
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“Version 2 introduces a multiagent system37,38 in which AI agents propose records to the expert serving as an oracle making the final labeling decision.”
From ASReview LAB v.2: Open-source text screening with multiple agents and a crowd of experts (2025) · cites this paper as [37]
Citations
Cited by
Multi-agent deep reinforcement learning: a survey
Gronauer, S., & Diepold, K. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-09996-w · Cited by 957
Decision Making in Multiagent Systems: A Survey
Rizk, Y., Awad, M., & Tunstel, E. W. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2840971 · Cited by 219
A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction
Bahram, M., Hubmann, C., Lawitzky, A., Aeberhard, M., & Wollherr, D. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2506642 · Cited by 175
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids
Chung, H.-M., Maharjan, S., Zhang, Y., & Eliassen, F. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3007167 · Cited by 120
Measuring the complexity of the law: the United States Code
Katz, D. M., & Bommarito, M. J. (2014) · Artificial Intelligence and Law · doi:10.1007/s10506-014-9160-8 · Cited by 117
Metadata
- Authors
- Shoham, Y., & Leyton-Brown, K.
- Publisher
- Cambridge University Press
- Published
- 2008-12-15
- Cited by
- 995
- References
- 0
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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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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. https://app.cochat.ai/papers/10.1017/cbo9780511811654
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- Shoham, Y., & Leyton-Brown, K. (2008). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. https://doi.org/10.1017/cbo9780511811654