Nash Convergence of Gradient Dynamics in General-Sum Games
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Approximate solutions to markov decision processes
Approximate solutions to markov decision processes
Efficient learning equilibrium
Artificial Intelligence
Walverine: a Walrasian trading agent
Decision Support Systems - Special issue: Decision theory and game theory in agent design
Multi-robot coordination and competition using mixed integer and linear programs
Multi-robot coordination and competition using mixed integer and linear programs
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
TacTex-05: a champion supply chain management agent
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
ATTac-2000: an adaptive autonomous bidding agent
Journal of Artificial Intelligence Research
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Simultaneous adversarial multi-robot learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Distributed planning in hierarchical factored MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Rewards for pairs of Q-learning agents conducive to turn-taking in medium-access games
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Shoham et al. identify several important agendas which can help direct research in multi-agent learning. We propose two additional agendas-called ''modelling'' and ''design''-which cover the problems we need to consider before our agents can start learning. We then consider research goals for modelling, design, and learning, and identify the problem of finding learning algorithms that guarantee convergence to Pareto-dominant equilibria against a wide range of opponents. Finally, we conclude with an example: starting from an informally-specified multi-agent learning problem, we illustrate how one might formalize and solve it by stepping through the tasks of modelling, design, and learning.