An architecture for multi-agent based self-adaptive system in mobile environment

Authors:
Seunghwa Lee;Jehwan Oh;Eunseok Lee
Affiliations:
School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

Citing 7
Cited 3

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Dynamic Configuration of Resource-Aware Services

Proceedings of the 26th International Conference on Software Engineering
Developing Adaptive Applications: The MOST Experience

Integrated Computer-Aided Engineering
User-Centric Content Negotiation for Effective Adaptation Service in Mobile Computing

IEEE Transactions on Software Engineering
MobiPADS: A Reflective Middleware for Context-Aware Mobile Computing

IEEE Transactions on Software Engineering
Context-Aware Middleware for Resource Management in the Wireless Internet

IEEE Transactions on Software Engineering
Context-aware communication

IEEE Wireless Communications

DIASCOPE: distributed adaptation system using cooperative proxies in ubiquitous network

ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
An adaptive mobile system using mobile grid computing in wireless network

ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part V
Multi-agent based hybrid system for dynamic web-content adaptation

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Conventional adaptive systems have common well-known constraints when attempting to normalize environment. An adaptive system must contain a certain number of rules allowing such a system to adapt to specific situations. If there is an absence of a rule in a new situation, the system cannot take appropriate action. Building and managing such complex static adaptive systems places an enormous burden on system developers. In this paper, we propose a multi-agent based intelligent adaptive system with a self-growing engine. In this system, the inference agent evaluates input context with specific factors and analyzes the results. The decision agent selects the most appropriate action among alternatives available for a specific context and intelligently evolves and adapts by means of a self-growing engine (SGE). The SGE can evaluate actions and generate new rules by applying it to a practical situation using remote video conferencing with mobile devices such as PDAs, and PCs.