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Found 10 papers, 3 papers with code
Rationality and connectivity in stochastic learning for networked coordination games
Our analysis shows that there is a relationship between the connectivity of the network, and the rationality of the agents.
Data-driven classification of low-power communication signals by an unauthenticated user using a software-defined radio
Many large-scale distributed multi-agent systems exchange information over low-power communication networks.
On the coordination efficiency of strategic multi-agent robotic teams
We study the problem of achieving decentralized coordination by a group of strategic decision makers choosing to engage or not in a task in a stochastic setting.
Robust one-shot estimation over shared networks in the presence of denial-of-service attacks
We consider the following scenario: multiple pairs of agents communicating strategically over shared communication networks in the presence of a jammer who may launch a denial-of-service.
Strategic multi-task coordination over regular networks of robots with limited computation and communication capabilities
Coordination is a desirable feature in multi-agent systems, allowing the execution of tasks that would be impossible by individual agents.
Robust remote estimation over the collision channel in the presence of an intelligent jammer
We consider a sensor-receiver pair communicating over a wireless channel in the presence of a jammer who may launch a denial-of-service attack.
Learning distributed channel access policies for networked estimation: data-driven optimization in the mean-field regime
The problem of communicating sensor measurements over shared networks is prevalent in many modern large-scale distributed systems such as cyber-physical systems, wireless sensor networks, and the internet of things.
Global games with Poisson observations: Bio-inspired distributed coordination of multi-agent systems
Global games are a class of incomplete information games where the payoffs exhibit strategic complementarity leading to an incentive for the agents to coordinate their actions.
Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization
In particular, we show that the method converges at a rate $O(log_2 k/\sqrt k)$ to the optimal solution, when the underlying objective function is strongly convex and smooth.
Data-driven sensor scheduling for remote estimation in wireless networks
Sensor scheduling is a well studied problem in signal processing and control with numerous applications.
