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 An Improved Bayesian Learning Method for Multi-agent System

2015 ◽  
Vol 11 (9) ◽  
pp. 82
Author(s):  
Shenghui Dai ◽  
Xueqin Zhu ◽  
Ying Gui ◽  
Hongzhen Xu
Keyword(s):  
Decision Making ◽  
Traffic Control ◽  
Bayesian Learning ◽  
Urban Traffic ◽  
Learning Method ◽  
Multi Agent System ◽  
Multi Agent ◽  
Traffic Control System ◽  
Urban Traffic Control ◽  
Recursive Modeling

A multi-agent coordinate ion is addressed in urban traffic control, which uses the recursive modeling method (RMM) that enables an agent to select its rational act ion by examining with other agents by modeling their decision making in a distributed multi-agent environment. Bayesian learning is used in conjunction with RMM for belief update. Based on this method, a multi-agent traffic control system is established and the results rated its effective.

scholarly journals INFLUENCE LEARNING FOR MULTI-AGENT SYSTEM BASED ON REINFORCEMENT LEARNING

2014 ◽  
pp. 39-44
Author(s):  
Anton Kabysh ◽  
Vladimir Golovko ◽  
Arunas Lipnickas
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Learning Approach ◽  
Multi Agent System ◽  
Agent System ◽  
Additional Information ◽  
Optimal Behavior ◽  
Learning Technique ◽  
Multi Agent

This paper describes a multi-agent influence learning approach and reinforcement learning adaptation to it. This learning technique is used for distributed, adaptive and self-organizing control in multi-agent system. This technique is quite simple and uses agent’s influences to estimate learning error between them. The best influences are rewarded via reinforcement learning which is a well-proven learning technique. It is shown that this learning rule supports positive-reward interactions between agents and does not require any additional information than standard reinforcement learning algorithm. This technique produces optimal behavior of multi-agent system with fast convergence patterns.

Improvement of learning method of multi-agent system by sharing learning data

2021 ◽  
Author(s):  
Qi Liu ◽  
Tomohiro Hayashida ◽  
Ichiro Nishizaki ◽  
Shinya Sekizaki
Keyword(s):  
Learning Method ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent ◽  
Learning Data
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