The Role Of Cooperation in Multi-Agent Learning

CKBS ’90 ◽  
1991 ◽  
pp. 164-180 ◽  
Author(s):  
Sati S. Sian
Keyword(s):  
Agent Learning ◽  
Multi Agent

Modeling and development of a multi-agent space for the secondary school

2020 ◽  
pp. 53-62
Author(s):  
I. K. Krasteva ◽  
T. A. Glushkova ◽  
S. N. Stoyanov
Keyword(s):  
Secondary School ◽  
Lifelong Learning ◽  
Industrial Revolution ◽  
Educational Systems ◽  
Fourth Industrial Revolution ◽  
Agent Learning ◽  
Multi Agent ◽  
Personal Assistants ◽  
The University

One of the guiding principles of the Fourth Industrial Revolution is the need for lifelong learning. This determines the growing role of intelligent educational systems to provide the necessary learning resources and services to users at any time and any place. This article presents the modeling and development of an intelligent multi-agent learning environment for the secondary school, developed by a team of the DeLC laboratory at the University of Plovdiv “Paisii Hilendarski”, Bulgaria. The learners are placed at the focus of the environment by personal assistants supporting work with the environment.

scholarly journals User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Giuseppe Caso ◽  
Ozgu Alay ◽  
Guido Carlo Ferrante ◽  
Luca De Nardis ◽  
Maria-Gabriella Di Benedetto ◽  
...  
Keyword(s):  
Game Theory ◽  
Learning Algorithms ◽  
Technology Selection ◽  
Access Technology ◽  
Radio Access Technology ◽  
Radio Access ◽  
Agent Learning ◽  
Multi Agent ◽  
User Centric ◽  
Radio Access Technology Selection

Hierarchical Reinforcement Learning

2021 ◽  
Vol 54 (5) ◽  
pp. 1-35
Author(s):  
Shubham Pateria ◽  
Budhitama Subagdja ◽  
Ah-hwee Tan ◽  
Chai Quek
Keyword(s):  
Reinforcement Learning ◽  
Future Research ◽  
Comprehensive Overview ◽  
Open Problems ◽  
Practical Applications ◽  
Hierarchical Reinforcement Learning ◽  
The Past ◽  
Agent Learning ◽  
Multi Agent ◽  
Supplementary Material

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.

Exploring the Role of Agent and Multi-Agent in Data Fusion Systems

2021 ◽  
Author(s):  
Shafiza Ariffin Kashinath ◽  
Salama A. Mostafa ◽  
Aida Mustapha ◽  
David Lim ◽  
Mohd Farhan Md Fudzee ◽  
...  
Keyword(s):  
Data Fusion ◽  
Fusion Systems ◽  
Multi Agent

scholarly journals The possible and the impossible in multi-agent learning

2007 ◽  
Vol 171 (7) ◽  
pp. 429-433 ◽  
Author(s):  
H. Peyton Young
Keyword(s):  
Agent Learning ◽  
Multi Agent

scholarly journals The brain attics: the strategic role of memory in single and multi-agent inquiry

Synthese ◽  
2018 ◽  
Vol 197 (3) ◽  
pp. 1203-1224
Author(s):  
Emmanuel J. Genot ◽  
Justine Jacot
Keyword(s):  
Strategic Role ◽  
Multi Agent ◽  
The Brain

scholarly journals Trust in multi-agent systems

2004 ◽  
Vol 19 (1) ◽  
pp. 1-25 ◽  
Author(s):  
SARVAPALI D. RAMCHURN ◽  
DONG HUYNH ◽  
NICHOLAS R. JENNINGS
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Comprehensive Treatment ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
The Core ◽  
Open Distributed Systems ◽  
Multi Agent

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

scholarly journals Deter and protect: crime modeling with multi-agent learning

2017 ◽  
Vol 4 (3) ◽  
pp. 155-169 ◽  
Author(s):  
Trevor R. Caskey ◽  
James S. Wasek ◽  
Anna Y. Franz
Keyword(s):  
Agent Learning ◽  
Multi Agent ◽  
Crime Modeling
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