scholarly journals A decentralized agent‐based semantic service control and self‐adaptation in smart health mobile applications

2021 ◽  
Author(s):  
Adel Alti ◽  
Abderrahim Lakehal ◽  
Philippe Roose
Keyword(s):  
Mobile Applications ◽  
Agent Based ◽  
Self Adaptation ◽  
Smart Health ◽  
Service Control ◽  
Semantic Service

S-Aframe: Agent-Based Multilayer Framework With Context-Aware Semantic Service for Vehicular Social Networks

2015 ◽  
Vol 3 (1) ◽  
pp. 44-63 ◽  
Author(s):  
Xiping Hu ◽  
Jidi Zhao ◽  
Boon-Chong Seet ◽  
Victor C. M. Leung ◽  
Terry H. S. Chu ◽  
...  
Keyword(s):  
Social Networks ◽  
Context Aware ◽  
Agent Based ◽  
Semantic Service

An agent-based self-adaptation architecture for implementing smart devices in Smart Space

2011 ◽  
Vol 52 (4) ◽  
pp. 2335-2346 ◽  
Author(s):  
Ingeol Chun ◽  
Jeongmin Park ◽  
Haeyoung Lee ◽  
Wontae Kim ◽  
Seungmin Park ◽  
...  
Keyword(s):  
Smart Devices ◽  
Smart Space ◽  
Agent Based ◽  
Self Adaptation

A Two-Layer Approach to Developing Self-Adaptive Multi-Agent Systems in Open Environment

2017 ◽  
pp. 585-606
Author(s):  
Xinjun Mao ◽  
Menggao Dong ◽  
Haibin Zhu
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Systems ◽  
Multi Agent Systems ◽  
Uncertain Environments ◽  
Agent Based ◽  
Fine Grain ◽  
Adaptation Mechanisms ◽  
Multi Agent ◽  
Self Adaptation ◽  
Self Adaptive

Development of self-adaptive systems situated in open and uncertain environments is a great challenge in the community of software engineering due to the unpredictability of environment changes and the variety of self-adaptation manners. Explicit specification of expected changes and various self-adaptations at design-time, an approach often adopted by developers, seems ineffective. This paper presents an agent-based approach that combines two-layer self-adaptation mechanisms and reinforcement learning together to support the development and running of self-adaptive systems. The approach takes self-adaptive systems as multi-agent organizations and enables the agent itself to make decisions on self-adaptation by learning at run-time and at different levels. The proposed self-adaptation mechanisms that are based on organization metaphors enable self-adaptation at two layers: fine-grain behavior level and coarse-grain organization level. Corresponding reinforcement learning algorithms on self-adaptation are designed and integrated with the two-layer self-adaptation mechanisms. This paper further details developmental technologies, based on the above approach, in establishing self-adaptive systems, including extended software architecture for self-adaptation, an implementation framework, and a development process. A case study and experiment evaluations are conducted to illustrate the effectiveness of the proposed approach.

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