Log-linear learning: Convergence in discrete and continuous strategy potential games

No abstract is available for this paper.

• Publication year

  2014

• Venue

  IEEE Conference on Decision and Control

• Publication date

  2014-12-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/CDC.2014.7039418
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Game Theory and Distributed Control

  2015cited by this paper
• Proving convergence of log-linear learning in potential games

  2014cited by this paper
• Designing games for distributed optimization

  2011cited by this paper
• Revisiting log-linear learning: Asynchrony, completeness and payoff-based implementation

  2010cited by this paper
• A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature by Tom Siegfried

  2009cited by this paper
• Cooperative Control and Potential Games

  2009cited by this paper
• Logit dynamic for continuous strategy games: existence of solutions

  2007cited by this paper
• Harris recurrence of Metropolis-within-Gibbs and trans-dimensional Markov chains

  2006cited by this paper
• General state space Markov chains and MCMC algorithms

  2004cited by this paper
• Approximation results for non-homogeneous markov chains and some applications

  2004cited by this paper
• Strategic Learning and Its Limits

  2004cited by this paper
• Probabilistic choice and procedurally bounded rationality

  2002cited by this paper
• An Introduction to Collective Intelligence

  1999cited by this paper
• REGULAR ARTICLEPotential Games

  1996cited by this paper
• Potential Games

  1994cited by this paper
• The Statistical Mechanics of Strategic Interaction

  1993cited by this paper
• Markov Chains — Theory and Applications

  1977cited by this paper
• ON TWO RECENT PAPERS ON ERGODICITY IN NONHOMOGENEOUS MARKOV CHAINS

  1972cited by this paper
• Weak ergodicity in non-homogeneous Markov chains

  1958cited by this paper
• Central Limit Theorem for Nonstationary Markov Chains. II

  1956influential reference

• ABRA: An algorithm which cannot converge to low-quality Nash equilibria

  2024cites this paper
• Toward the minimum vertex cover of complex networks using distributed potential games

  2022cites this paper
• Self-Adaptive Game Theoretic Approaches for Resource Allocation in SC-FDMA Based Heterogeneous Networks

  2022cites this paper
• Learning in Potential Games for Electric Power Grids: Models, Dynamics, and Outlook

  2022cites this paper
• The Price of Anarchy is Fragile in Single-Selection Coverage Games

  2021cites this paper
• Toward Refined Nash Equilibria for the SET K-COVER Problem via a Memorial Mixed-Response Algorithm

  2021cites this paper
• Potential game for dynamic task allocation in multi-agent system.

  2020cites this paper
• Stochastic Learning for the SET K-COVER Problem in Heterogeneous Wireless Sensor Networks

  2020cites this paper
• Energy-Efficient Resource Allocation for Energy Harvesting-Based Device-to-Device Communication

  2019cites this paper
• Potential Game Theoretic Learning for the Minimal Weighted Vertex Cover in Distributed Networking Systems

  2019cites this paper
• Robust Methods for Influencing Strategic Behavior

  2018cites this paper
• A Time Variant Log-Linear Learning Approach to the SET K-COVER Problem in Wireless Sensor Networks

  2018influential citation
• Logit Dynamics in Potential Games with Memoryless Players

  2017cites this paper
• Optimising Wireless Sensor Network Link Quality Through Power Control with Non-convex Utilities Using Game Theory

  2017cites this paper
• Energy-Efficient Optimization for Device-to-Device Communication Underlaying Cellular Networks

  2017cites this paper
• Are Multiagent Systems Resilient to Communication Failures?

  2017cites this paper
• Learning of cooperative behaviour in robot populations

  2016cites this paper
• Markov decision evolutionary game theoretic learning for cooperative sensing of unmanned aerial vehicles

  2015cites this paper