Naive asymptotics for hitting time bounds in Markov chains
Acta Informatica
Implicit Negotiation in Repeated Games
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Dispersion games: general definitions and some specific learning results
Eighteenth national conference on Artificial intelligence
Minority Games: Interacting Agents in Financial Markets (Oxford Finance Series)
Minority Games: Interacting Agents in Financial Markets (Oxford Finance Series)
Settling the Complexity of Two-Player Nash Equilibrium
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Autonomous Agents and Multi-Agent Systems
Computing correlated equilibria in multi-player games
Journal of the ACM (JACM)
THE ALOHA SYSTEM: another alternative for computer communications
AFIPS '70 (Fall) Proceedings of the November 17-19, 1970, fall joint computer conference
Essentials of Game Theory: A Concise, Multidisciplinary Introduction
Essentials of Game Theory: A Concise, Multidisciplinary Introduction
Attachment Learning for Multi-channel Allocation in Distributed OFDMA Networks
ICPADS '11 Proceedings of the 2011 IEEE 17th International Conference on Parallel and Distributed Systems
Full length article: Minority game for cognitive radios: Cooperating without cooperation
Physical Communication
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To achieve an optimal outcome in many situations, agents need to choose distinct actions from one another. This is the case notably in many resource allocation problems, where a single resource can only be used by one agent at a time. How shall a designer of a multi-agent system program its identical agents to behave each in a different way? From a game theoretic perspective, such situations lead to undesirable Nash equilibria. For example consider a resource allocation game in that two players compete for an exclusive access to a single resource. It has three Nash equilibria. The two pure-strategy NE are efficient, but not fair. The one mixed-strategy NE is fair, but not efficient. Aumann's notion of correlated equilibrium fixes this problem: It assumes a correlation device that suggests each agent an action to take. However, such a "smart" coordination device might not be available. We propose using a randomly chosen, "stupid" integer coordination signal. "Smart" agents learn which action they should use for each value of the coordination signal. We present a multi-agent learning algorithm that converges in polynomial number of steps to a correlated equilibrium of a channel allocation game, a variant of the resource allocation game. We show that the agents learn to play for each coordination signal value a randomly chosen pure-strategy Nash equilibrium of the game. Therefore, the outcome is an efficient correlated equilibrium. This CE becomes more fair as the number of the available coordination signal values increases.