Adaptive and Dynamic Service Composition via Multi-agent Reinforcement Learning

2014 ◽  
Author(s):  
Hongbing Wang ◽  
Qin Wu ◽  
Xin Chen ◽  
Qi Yu ◽  
Zibin Zheng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Service Composition ◽  
Dynamic Service Composition ◽  
Multi Agent

A multi-agent reinforcement learning approach to dynamic service composition

2016 ◽  
Vol 363 ◽  
pp. 96-119 ◽  
Author(s):  
Hongbing Wang ◽  
Xiaojun Wang ◽  
Xingguo Hu ◽  
Xingzhi Zhang ◽  
Mingzhu Gu
Keyword(s):  
Reinforcement Learning ◽  
Service Composition ◽  
Learning Approach ◽  
Dynamic Service Composition ◽  
Multi Agent

Agent-Based Infrastructure for Dynamic Composition of Grid Services

2010 ◽  
pp. 911-936
Author(s):  
Matthew Adigun ◽  
Johnson Iyilade ◽  
Klaas Kabini
Keyword(s):  
Service Composition ◽  
Research Effort ◽  
Multi Agent System ◽  
Grid Services ◽  
Agent System ◽  
Agent Based ◽  
Dynamic Composition ◽  
Emerging Trends ◽  
Dynamic Service Composition ◽  
Multi Agent

The service-oriented computing paradigm is based on the assumption that existing services can be put together in order to obtain new composite services. This chapter focuses on how peer-to-peer architectures based on multi-agent systems can be used to build highly dynamic and reconfigurable infrastructure that support dynamic composition of grid services. The chapter starts by providing an overview of key technologies for SOC. It then introduces dynamic service composition and challenges of composing grid services. The authors further motivate for Multi-agent system approach in SOC and why it becomes important in service composition. They then present our research effort, AIDSEC, an agent-based infrastructure for dynamic service composition, describing its architecture, implementation and comparison with some related work in the literature. In addition, the chapter raises some emerging trends in SOC and the particular challenges they pose to service composition. They conclude by suggesting that a solution based on multi-agent system is required for composing services that possess capabilities of autonomy, reliability, flexibility, and robustness.

Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition

2017 ◽  
Vol 12 (2) ◽  
pp. 1-42 ◽  
Author(s):  
Hongbign Wang ◽  
Xin Chen ◽  
Qin Wu ◽  
Qi Yu ◽  
Xingguo Hu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Service Composition ◽  
Multi Agent

Effective service composition using multi-agent reinforcement learning

2016 ◽  
Vol 92 ◽  
pp. 151-168 ◽  
Author(s):  
Hongbing Wang ◽  
Xiaojun Wang ◽  
Xingzhi Zhang ◽  
Qi Yu ◽  
Xingguo Hu
Keyword(s):  
Reinforcement Learning ◽  
Service Composition ◽  
Multi Agent ◽  
Effective Service
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