Learning automata: an introduction
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The weighted majority algorithm
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Temporal difference learning and TD-Gammon
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On No-Regret Learning, Fictitious Play, and Nash Equilibrium
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Multi-agent algorithms for solving graphical games
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The empirical Bayes envelope and regret minimization in competitive Markov decision processes
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R-max - a general polynomial time algorithm for near-optimal reinforcement learning
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Nash q-learning for general-sum stochastic games
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Efficient learning equilibrium
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Complexity of (iterated) dominance
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Mobilized Ad-Hoc Networks: A Reinforcement Learning Approach
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FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Efficient algorithms for online decision problems
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Computing the optimal strategy to commit to
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Finding equilibria in large sequential games of imperfect information
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
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Performance bounded reinforcement learning in strategic interactions
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Simple search methods for finding a Nash equilibrium
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Algorithms for rationalizability and CURB sets
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A generalized strategy eliminability criterion and computational methods for applying it
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Mixed-integer programming methods for finding Nash equilibria
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Optimal efficient learning equilibrium: imperfect monitoring in symmetric games
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Complexity results about Nash equilibria
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Fast concurrent reinforcement learners
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Learning against opponents with bounded memory
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Value-function reinforcement learning in Markov games
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A note on strategy elimination in bimatrix games
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Multidimensional screening: online computation and limited information
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Task-technology fit and user acceptance of online auction
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Towards a taxonomy of decision making problems in multi-agent systems
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Approximation guarantees for fictitious play
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I lay out a slight refinement of Shoham et al.'s taxonomy of agendas that I consider sensible for multiagent learning (MAL) research. It is not intended to be rigid: senseless work can be done within these agendas and additional sensible agendas may arise. Within each agenda, I identify issues and suggest directions. In the computational agenda, direct algorithms are often more efficient, but MAL plays a role especially when the rules of the game are unknown or direct algorithms are not known for the class of games. In the descriptive agenda, more emphasis should be placed on establishing what classes of learning rules actually model learning by multiple humans or animals. Also, the agenda is, in a way, circular. This has a positive side too: it can be used to verify the learning models. In the prescriptive agendas, the desiderata need to be made clear and should guide the design of MAL algorithms. The algorithms need not mimic humans' or animals' learning. I discuss some worthy desiderata; some from the literature do not seem well motivated. The learning problem is interesting both in cooperative and noncooperative settings, but the concerns are quite different. For many, if not most, noncooperative settings, future work should increasingly consider the learning itself strategically. Lower bounds cut across the agendas. They can be derived on the computational complexity and on the number of interactions needed.