Add to ORKGWe study the question of how a local learning algorithm, executed by multiple distributed agents, can lead to a global system of communication. First, the notion of a perfect communication system is defined. Next, two measures of communication system quality are specified. It is shown that maximization of these measures leads to perfect communication production. Based on this principle, local adaptation rules for communication development are constructed. The resulting stochastic algorithm is validated in computational experiments. Empirical analysis indicates that a mild degree of stochasticity is instrumental in reaching states that correspond to accurate communication.The first author gratefully acknowledges a Fulbright grant.Peer review...
In modern day machine learning applications such as self-driving cars, recommender systems, robotics...
This dissertation is concerned with distributed decision making in networked multi-agent systems; th...
Learning to communicate is an emerging challenge in AI research. It is known that agents interacting...
We study the question of how a local learning algorithm, executed by multiple distributed agents, ca...
We consider distributed online learning protocols that control the exchange of in-formation between ...
Distributed machine learning bridges the traditional fields of distributed systems and machine learn...
Abstract: We consider the classical TD(0) algorithm implemented on a net-work of agents wherein the ...
In distributed optimization and machine learning, multiple nodes coordinate to solve large problems....
We consider a wireless communication system in which N transmitter-receiver pairs want to communicat...
This paper attempts to bridge the fields of ma-chine learning, robotics, and distributed AI. It disc...
A protocol for distributed estimation of discrete distributions is proposed. Each agent begins with ...
Distributed learning is the iterative process of decision-making in the presence of other decision-m...
Modern cyber-physical architectures use data col-lected from systems at different physical locations...
A distributed system is composed of independent agents, machines, processing units, etc., where inte...
The decentralized nature of multi-Agent learning often requires continuous information exchange over...
In modern day machine learning applications such as self-driving cars, recommender systems, robotics...
This dissertation is concerned with distributed decision making in networked multi-agent systems; th...
Learning to communicate is an emerging challenge in AI research. It is known that agents interacting...
We study the question of how a local learning algorithm, executed by multiple distributed agents, ca...
We consider distributed online learning protocols that control the exchange of in-formation between ...
Distributed machine learning bridges the traditional fields of distributed systems and machine learn...
Abstract: We consider the classical TD(0) algorithm implemented on a net-work of agents wherein the ...
In distributed optimization and machine learning, multiple nodes coordinate to solve large problems....
We consider a wireless communication system in which N transmitter-receiver pairs want to communicat...
This paper attempts to bridge the fields of ma-chine learning, robotics, and distributed AI. It disc...
A protocol for distributed estimation of discrete distributions is proposed. Each agent begins with ...
Distributed learning is the iterative process of decision-making in the presence of other decision-m...
Modern cyber-physical architectures use data col-lected from systems at different physical locations...
A distributed system is composed of independent agents, machines, processing units, etc., where inte...
The decentralized nature of multi-Agent learning often requires continuous information exchange over...
In modern day machine learning applications such as self-driving cars, recommender systems, robotics...
This dissertation is concerned with distributed decision making in networked multi-agent systems; th...
Learning to communicate is an emerging challenge in AI research. It is known that agents interacting...