69 citations · 278 across the 38 of their papers we have counts for
11 papers · 1 filter
Distributed Learning Dynamics Converging to the Core of -Matchings
Aya Hamed, Jeff S. Shamma
-Matching is a special case of matching problems where nodes can join multiple matchings with the degree of each node constrained by an upper bound, the node's -value. The co…
Equilibrium Selection for Multi-agent Reinforcement Learning: A Unified Framework
Runyu Zhang, Gioele Zardini, Asuman Ozdaglar +2
While multi-agent reinforcement learning (MARL) has produced numerous algorithms that converge to Nash or related equilibria, such equilibria are often non-unique and can exhibit w…
Distributed Learning Dynamics for Coalitional Games
Aya Hamed, Jeff S. Shamma
In the framework of transferable utility coalitional games, a scoring (characteristic) function determines the value of any subset/coalition of agents. Agents decide on both which…
Higher-Order Uncoupled Dynamics Do Not Lead to Nash Equilibrium -- Except When They Do
Sarah A. Toonsi, Jeff S. Shamma
The framework of multi-agent learning explores the dynamics of how individual agent strategies evolve in response to the evolving strategies of other agents. Of particular interest…
Smart Jammer and LTE Network Strategies in An Infinite-Horizon Zero-Sum Repeated Game with Asymmetric and Incomplete Information
Farhan M. Aziz, Lichun Li, Jeff S. Shamma +1
LTE/LTE-Advanced networks are known to be vulnerable to denial-of-service and loss-of-service attacks from smart jammers. In this article, the interaction between a smart jammer an…
Security Strategies of Both Players in Asymmetric Information Zero-Sum Stochastic Games with an Informed Controller
Lichun Li, Cedric Langbort, Jeff S. Shamma
This paper considers a zero-sum two-player asymmetric information stochastic game where only one player knows the system state, and the transition law is controlled by the informed…
An LP Approach for Solving Two-Player Zero-Sum Repeated Bayesian Games
Lichun Li, Cedric Langbort, Jeff Shamma
This paper studies two-player zero-sum repeated Bayesian games in which every player has a private type that is unknown to the other player, and the initial probability of the type…
Efficient Strategy Computation in Zero-Sum Asymmetric Repeated Games
Lichun Li, Jeff S. Shamma
Zero-sum asymmetric games model decision making scenarios involving two competing players who have different information about the game being played. A particular case is that of n…
BLMA: A Blind Matching Algorithm with Application to Cognitive Radio Networks
Doha Hamza, Jeff S. Shamma
We consider a two-sided matching problem with a defined notion of pairwise stability. We propose a distributed blind matching algorithm (BLMA) to solve the problem. We prove the so…
Learning Efficient Correlated Equilibria
Holly P. Borowski, Jason R. Marden, Jeff S. Shamma
The majority of distributed learning literature focuses on convergence to Nash equilibria. Correlated equilibria, on the other hand, can often characterize more efficient collectiv…
Aspiration Learning in Coordination Games
Georgios C. Chasparis, Ari Arapostathis, Jeff S. Shamma
We consider the problem of distributed convergence to efficient outcomes in coordination games through dynamics based on aspiration learning. Under aspiration learning, a player co…