scholarly journals Collaborative Agent Learning Using Neurocomputing

2004 ◽  
pp. 619-624
Author(s):  
Saulat Farooque ◽  
Ajith Abraham ◽  
Lakhmi Jain
Keyword(s):  
Agent Learning ◽  
Collaborative Agent

scholarly journals Of pride and prejudice: agent learning under sticky and persistent stereotype

2021 ◽  
Vol 16 (2) ◽  
pp. 381-410
Author(s):  
Georg D. Blind ◽  
Stefania Lottanti von Mandach
Keyword(s):  
Maximum Likelihood ◽  
Private Equity ◽  
Optimum Level ◽  
Pride And Prejudice ◽  
Anglo Saxon ◽  
Agent Learning ◽  
Economic Interaction ◽  
Level Of Effort ◽  
Group Agents ◽  
A Share

AbstractStereotypes matter for economic interaction if counterparty utility is informed by factors other than price. Stereotyped agents may engage in efforts to counter stereotype by adapting to in-group standards. We present a model informing the optimal extent of these efforts depending on an agent’s (a) share of total transactions between out- and in-group agents; and (b) share of repeated transaction pairings with in-group counterparties. Low values of (a) suppress the effect of adaptation efforts on the stereotype itself (persistence). In turn, low values of (b) mean that out-group agents cannot dissociate from stereotype (stickiness). Significantly, the model implies that the optimum level of effort may require adaptation beyond in-group standards, and that such over-adaptation attains maximum likelihood in cases where stereotype is sticky and persistent at the same time. We test our model with data on private equity buyout investments conducted in Japan between 1998 and 2015 by domestic Japanese and Anglo-Saxon funds. We document that the latter not only adapt, but eventually over-adapt. In addition, we show that their efforts are effective in reducing a premium initially asked by domestic counterparties.

A Microservice-driven Collaborative Agent in Virtual Learning Environments: A Role Model for a Tracing Agent

2020 ◽  
Author(s):  
Jéferson Miguel Thalheimer ◽  
Aluizio Haendchen Filho ◽  
Fabio Julio Pereira Briks ◽  
Rafael Castaneda Ribeiro ◽  
Fernando Concatto ◽  
...  
Keyword(s):  
Learning Environments ◽  
Role Model ◽  
Virtual Learning ◽  
Virtual Learning Environments ◽  
Collaborative Agent

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.

Learning Summarised Messaging Through Mediated Differentiable Inter-Agent Learning

2020 ◽  
pp. 546-557
Author(s):  
Sharan Gopal ◽  
Rishabh Mathur ◽  
Shaunak Deshwal ◽  
Anil Singh Parihar
Keyword(s):  
Agent Learning

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

Multi-agent Learning Algorithms

2017 ◽  
pp. 860-863
Author(s):  
Yoav Shoham ◽  
Rob Powers
Keyword(s):  
Learning Algorithms ◽  
Agent Learning ◽  
Multi Agent
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