scholarly journals Distributed Multichannel Access in High-Frequency Diversity Networks: A Multi-Agent Learning Approach With Correlated Equilibrium

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 85581-85593
Author(s):  
Wen Li ◽  
Yuhua Xu ◽  
Yunpeng Cheng ◽  
Yang Yang ◽  
Xueqiang Chen ◽  
...  
Keyword(s):  
High Frequency ◽  
Correlated Equilibrium ◽  
Learning Approach ◽  
Frequency Diversity ◽  
Agent Learning ◽  
Multi Agent ◽  
Multichannel Access

MAS Learning Based on Bayesian Learning Method

2010 ◽  
Vol 20-23 ◽  
pp. 1292-1298
Author(s):  
De Jia Shi ◽  
Zhi Qiang Liu ◽  
Jing He
Keyword(s):  
Learning Strategies ◽  
Bayesian Learning ◽  
Free Text ◽  
Learning Approach ◽  
Learning Method ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Learning ◽  
Multi Agent ◽  
Keyword Searches

Mulit-agent system[MAS] research on learning has been in the area of negotiation, and learning strategies of other agents.This paper presents an agent learning approach in multi-agent system based on Bayesian learning, it researches to develop agents that learn free-text queries and keyword searches in MAS. The MAS learns to identify an appropriate agent to answer free-text and natural language queries as well as keyword searches submitted by users. The paper describes how Bayesian learning is implemented in MAS, and analyzes the effectiveness of MAS learning based on the Bayesian learning approach by analyzing the accuracy and degree of learning.

scholarly journals Multi-Agent Learning Approach for UAVs Enabled Wireless Networks

2021 ◽  
Author(s):  
Lorenzo De Simone ◽  
Yongxu Zhu ◽  
Wenchao Xia ◽  
Tasos Dagiuklas ◽  
Kai Kit Wong
Keyword(s):  
Wireless Networks ◽  
Learning Approach ◽  
Agent Learning ◽  
Multi Agent

scholarly journals Decentralized Anti-coordination Through Multi-agent Learning

2013 ◽  
Vol 47 ◽  
pp. 441-473 ◽  
Author(s):  
L. Cigler ◽  
B. Faltings
Keyword(s):  
Resource Allocation ◽  
Nash Equilibria ◽  
Learning Algorithm ◽  
Channel Allocation ◽  
Pure Strategy ◽  
Correlated Equilibrium ◽  
Agent Learning ◽  
Multi Agent ◽  
Resource Allocation Game ◽  
The One

To achieve an optimal outcome in many situations, agents need to choose distinct actions from one another. This is the case notably in many resource allocation problems, where a single resource can only be used by one agent at a time. How shall a designer of a multi-agent system program its identical agents to behave each in a different way? From a game theoretic perspective, such situations lead to undesirable Nash equilibria. For example consider a resource allocation game in that two players compete for an exclusive access to a single resource. It has three Nash equilibria. The two pure-strategy NE are efficient, but not fair. The one mixed-strategy NE is fair, but not efficient. Aumann's notion of correlated equilibrium fixes this problem: It assumes a correlation device that suggests each agent an action to take. However, such a "smart" coordination device might not be available. We propose using a randomly chosen, "stupid" integer coordination signal. "Smart" agents learn which action they should use for each value of the coordination signal. We present a multi-agent learning algorithm that converges in polynomial number of steps to a correlated equilibrium of a channel allocation game, a variant of the resource allocation game. We show that the agents learn to play for each coordination signal value a randomly chosen pure-strategy Nash equilibrium of the game. Therefore, the outcome is an efficient correlated equilibrium. This CE becomes more fair as the number of the available coordination signal values increases.

Sign in / Sign up

Export Citation Format

Share Document