The complexity of eliminating dominated strategies
Mathematics of Operations Research
Communication complexity
Algorithms, games, and the internet
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Multiagent learning using a variable learning rate
Artificial Intelligence
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Nash Convergence of Gradient Dynamics in General-Sum Games
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A polynomial-time nash equilibrium algorithm for repeated games
Proceedings of the 4th ACM conference on Electronic commerce
Some complexity questions related to distributive computing(Preliminary Report)
STOC '79 Proceedings of the eleventh annual ACM symposium on Theory of computing
A continuation method for Nash equilibria in structured games
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Perspectives on multiagent learning
Artificial Intelligence
The communication complexity of uncoupled nash equilibrium procedures
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
ACM SIGACT News
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
SIAM Journal on Computing
On the communication complexity of approximate nash equilibria
SAGT'12 Proceedings of the 5th international conference on Algorithmic Game Theory
Hi-index | 0.00 |
A fast-growing body of research in the AI and machine learning communities addresses learning in games, where there are multiple learners with different interests. This research adds to more established research on learning in games conducted in economics. In part because of a clash of fields, there are widely varying requirements on learning algorithms in this domain. The goal of this paper is to demonstrate how communication complexity can be used as a lower bound on the required learning time or cost. Because this lower bound does not assume any requirements on the learning algorithm, it is universal, applying under any set of requirements on the learning algorithm.We characterize exactly the communication complexity of various solution concepts from game theory, namely Nash equilibrium, iterated dominant strategies (both strict and weak), and backwards induction. This gives the tighest lower bounds on learning in games that can be obtained with this method.