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Conjectural Equilibrium in Multiagent Learning
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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 22,188
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
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
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
Capital-Labor Substitution and Economic Efficiency
Arrow, K. J., Chenery, H. B., Minhas, B. S., & Solow, R. M. H. (1961) · The Review of Economics and Statistics · doi:10.2307/1927286 · Cited by 2,229
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
Handbook of Global Optimization
Horst, R., & Pardalos, P. M. (1995) · Nonconvex optimization and its applications · doi:10.1007/978-1-4615-2025-2 · Cited by 1,638
Game Theory for Applied Economists
Gibbons, R. S. (1992) · Princeton University Press eBooks · doi:10.1515/9781400835881 · Cited by 1,486
Introduction to Global Optimization
Horst, R., Pardalos, P. M., & Thoại, N. V. (2000) · Nonconvex optimization and its applications · doi:10.1007/978-1-4615-0015-5 · Cited by 1,232
Competitive Markov Decision Processes
Filar, J. A., & Vrieze, K. (1996) · Springer eBooks · doi:10.1007/978-1-4612-4054-9 · Cited by 1,202
The dynamics of reinforcement learning in cooperative multiagent systems
Claus, C., & Boutilier, C. E. (1998) · Cited by 1,094
de Mélo, J., Shoven, J. B., & Whalley, J. (1993) · The Economic Journal · doi:10.2307/2234492 · Cited by 1,020
Game Theory for Applied Economists
Gibbons, R. D. (1992) · Princeton University Press eBooks · doi:10.2307/j.ctvcmxrzd · Cited by 929
Spivak, M. (2018) · CRC Press eBooks · doi:10.1201/9780429501906 · Cited by 812
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
Hu, J., & Wellman, M. P. (1998) · Cited by 759
Self-citationYoung, H. P. (1996) · The Journal of Economic Perspectives · doi:10.1257/jep.10.2.105 · Cited by 540
Wellman, M. P. (1993) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2 · Cited by 527
Self-citationFudenberg, D., & Levine, D. K. (1993) · Econometrica · doi:10.2307/2951716 · Cited by 523
Glycopantis, D., & Takayama, A. (1976) · Economica · doi:10.2307/2553143 · Cited by 422
Social Stability and Equilibrium
Gilboa, I., & Matsui, A. (1991) · Econometrica · doi:10.2307/2938230 · Cited by 347
Adaptive and sophisticated learning in normal form games
Milgrom, P., & Roberts, J. H. (1991) · Games and Economic Behavior · doi:10.1016/0899-8256(91)90006-z · Cited by 343
On the emergence of social conventions: modeling, analysis, and simulations
Shoham, Y., & Tennenholtz, M. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00028-3 · Cited by 290
Starting Point Bias in Contingent Valuation Bidding Games
Boyle, K., Bishop, R. C., & Welsh, M. (1985) · Land Economics · doi:10.2307/3145811 · Cited by 263
The Stability of a Competitive Economy: A Survey Article
Negishi, T. (1962) · Econometrica · doi:10.2307/1909319 · Cited by 252
Knowledge and Equilibrium in Games
Brandenburger, A. M. (1992) · The Journal of Economic Perspectives · doi:10.1257/jep.6.4.83 · Cited by 168
The WALRAS Algorithm: A Convergent Distributed Implementation of General Equilibrium Outcomes
Cheng, J. Q., & Wellman, M. P. (1998) · Computational Economics · doi:10.1023/a:1008654125853 · Cited by 165
Self-citationIntroduction to Global Optimization.
Tofallis, C., Horst, R., Padalos, P. M., & Thoai, N. V. (1996) · Journal of the Operational Research Society · doi:10.2307/3010143 · Cited by 157
Dynamics of computational ecosystems
Kephart, J. O., Hogg, T., & Huberman, B. A. (1989) · Physical Review A · doi:10.1103/physreva.40.404 · Cited by 150
Learning to coordinate actions in multi-agent systems
Weiß, G. (1993) · International Joint Conference on Artificial Intelligence · Cited by 110
Applying General EquilibriumPreprint
Shoven, J. B., & Whalley, J. (1992) · RePEc: Research Papers in Economics · Cited by 98
A computational market model for distributed configuration design
Wellman, M. P. (1995) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s089006040000216x · Cited by 94
Self-citationOnline learning about other agents in a dynamic multiagent system
Hu, J., & Wellman, M. P. (1998) · Proceedings of the second international conference on Autonomous agents - AGENTS '98 · doi:10.1145/280765.280839 · Cited by 84
Self-citationExercises in Conjectural Equilibria
Hahn, F. H. (1978) · Palgrave Macmillan UK eBooks · doi:10.1007/978-1-349-03917-3_4 · Cited by 77
Exercises in Conjectural Equilibria
Hahn, F. H. (1977) · Scandinavian Journal of Economics · doi:10.2307/3439508 · Cited by 71
Takayama, A. (1991) · Cited by 60
A Simple Computational Market for Network Information Services
Mullen, T., & Wellman, M. P. (1995) · Cited by 59
Self-citationLearning nested agent models in an information economy
Vidal, J. M., & Durfee, E. H. (1998) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/095281398146770 · Cited by 47
On the Gains and Losses of Speculation in Equilibrium Markets
Sandholm, T., & Ygge, F. (1997) · Cited by 45
Agents Learning about Agents: A Framework and Analysis
Vidal, M. C., & Durfee, E. H. (1997) · Cited by 37
Self-fulfilling Bias in Multiagent Learning
Hu, J., & Wellman, M. P. (1996) · Cited by 34
Self-citationLearning Conventions in Multiagent Stochastic Domains using Likelihood EstimatesPreprint
Boutilier, C. E. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.3561 · Cited by 25
IJCAI-95 Workshop on Adaptation and Learning in Multiagent Systems
Sen, S. (1996) · AI Magazine · doi:10.1609/aimag.v17i1.1215 · Cited by 15
A NOTE ON THE EXISTENCE OF STARTING POINT BIAS IN ITERATIVE BIDDING GAMES
Samples, K. C., & Samples, K. C. (1985) · RePEc: Research Papers in Economics · doi:10.22004/ag.econ.32510 · Cited by 14
Flagged first, then by citations
3 of the 46 references couldn’t be matched to a known paper.
Research works printed but not linked: 12
- Boutilier, C. (1996). Learningconventions in multiagent stochastic domains using likelihood estimates. Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, (pp. 106–114). Portland, OR.
- Cliff, D. (1998).Evolving parameter sets for adaptive trading agents in continuous double-auction markets. Agents-98 Workshop on Artificial Societies and Computational Markets, (pp. 38–47). Minneapolis, MN.
- Fudenberg, D. & Tirole, J. (1991). Game Theory. MIT Press.
- Grefenstette, J.J., et al. (Eds.). (1996). AAAI Spring Symposium onAdaptation, Coevolution, and Learning in Multiagent Systems. AAAI Press.
- Hu, J & Wellman, M.P. (1996). Self-fulfillingbias in multiagent learning. Second International Conference on Multiagent Systems, (pp. 118–125). Kyoto, Japan.
- Hu, J & Wellman, M.P. (1998b). Multiagentreinforcement learning: Theoretical framework and an algorithm. Fifteenth International Conference on Machine Learning, (pp. 242–250). Madison, WI.
- Mullen, T. & Wellman, M.P. (1995). A simple computational market for networkinformation services. First International Conference on Multiagent Systems, (pp. 283–289). San Francisco, CA.
- Ono, N. & Fukumoto, K. (1996). Multi-agent reinforcement learning: A modular approach. Second International Conference on Multiagent Systems, (pp. 252–258). Kyoto, Japan.
- Sen, S. (Ed.). (1997). AAAI-97 Workshop on MultiagentLearning. AAAI Press.
- Shoven, J.B. & Whalley, J. (1992). Applying GeneralEquilibrium. Cambridge University Press.
- Weiß, G. (1993). Learning to coordinateactions in multi-agent systems. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, (pp. 311–316).
- Wellman, M.P. (1995). Acomputational market model for distributed configuration design. AI EDAM, 9, 125–133.
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Metadata
- Authors
- Wellman, M. P., & Hu, J.
- Affiliations
- University of Michigan (US)
- Venue
- Machine Learning
- Published
- 1998-11
- Volume
- 33
- Issue
- 2-3
- Pages
- 179-200
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2117138095
- Cited by
- 75
- References
- 46
- Citations by year
- 2023: 1 · 2021: 1 · 2019: 1 · 2017: 1 · 2016: 2 · 2015: 1 · 2014: 4 · 2013: 4 · 2012: 4
- FWCI
- 8.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- multi-agent learning, strategic learning, competitive behavior, convergence
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