Mimicking Complexity : Automatic Generation of Models for the Development of Self-Adaptive Systems

International audience Many methods for complex systems control use a black box approach where the internal states and mechanisms of the controlled process are not needed to be known. Usually, such systems are tested on simulations before their validation on the real world process they were made for...

Full description

Bibliographic Details
Main Authors: Boes, Jérémy, Glize, Pierre, Migeon, Frédéric
Other Authors: Systèmes Multi-Agents Coopératifs (IRIT-SMAC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)
Format: Conference Object
Language:English
Published: HAL CCSD 2013
Subjects:
Complex systems modeling
Multi-agent systems
Self-tuning
Self-composition
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Iceland
Online Access:https://hal.science/hal-04083848
https://hal.science/hal-04083848/document
https://hal.science/hal-04083848/file/boes_12475.pdf
Description
Summary:International audience Many methods for complex systems control use a black box approach where the internal states and mechanisms of the controlled process are not needed to be known. Usually, such systems are tested on simulations before their validation on the real world process they were made for. These simulations are based on sharp analytical models of the target process that can be very difficult to obtain. But is it useful in the case of black box methods? Since the control system only sees inputs and outputs and is able to learn, we only need to mimic the typical features of the process (such as non-linearity, interdependencies, etc) in an abstract way. This paper aims to show how a simple and versatile simulator can help the design of systems that have to deal with complexity. We present a generator of models used in the simulator and discuss the results obtained in the case of the design of a control system for heat engines.

Similar Items