Self-Adaptive Model Generation for Ambient Systems

被引：7

作者：

Nigon, Julien ^{[1
]}

Gleizes, Marie-Pierre ^{[1
]}

Migeon, Frederic ^{[1
]}

机构：

[1] Univ Fed Toulouse, 118 Rte Narbonne, F-31062 Toulouse, France

来源：

7TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2016) / THE 6TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2016) / AFFILIATED WORKSHOPS | 2016年 / 83卷

关键词：

Self-Organisation; Context; Learning; Adaptation; Multi-Agent System; Cooperation; Pattern recognition;

D O I：

10.1016/j.procs.2016.04.150

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Ambient systems are composed of many interacting entities, and their behaviour is constantly changing. Under these conditions, static models are insufficient to understand and control such systems. In this paper, we investigate the possibility to generate real-time dynamic model of an ambient system. For this, we present AMOEBA, a multi-agent system designed to address this problem. It is based on a set of cooperative mechanisms from the Adaptive Multi-Agent System theory. Experiments on simulated physical systems highlight the interesting properties of AMOEBA. (C) 2016 The Authors. Published by Elsevier B.V.

引用

页码：675 / 679

页数：5

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