Inproceedings
Book title :
Modelling and Implementation of Complex Systems
Series :
Lecture Notes in Networks and Systems
Editors : Chikhi Salim; Diaz-Descalzo Gregorio; Amine Abdelmalek; Chaoui Allaoua; Saidouni Djamel Eddine; Kholladi Mohamed Khireddine
Address :
Mostaganem, Algeria
ISBN : 978-3-031-18516-8
Publisher :
Information
Period : October 2022
Pages : 220--233
Details
An Example of a Dynamic CPN Model to Obtain Routes in the Presence of Obstacles Detected Using Machine Learning Techniques
Bouzenada Ahmed Bouhamed Mohammed Mounir Kamel Oussama Macià Hermenegilda Díaz Gregorio Chaoui Allaoua
Obtaining routes has been a challenge during the recent history of software systems. GPS has helped this technology to be widely used. Some challenges still remain like providing routes in the presence of obstacles. The term obstacle differs depending on the scenario. It is not the same for wheelchair users than for a group of school children. In this paper, we study how to enable a set of technologies to bridge this gap. We propose to combine a graphical formalism with machine learning. Color Petri Nets are used to model a dynamic scenario where conditions, i.e. obstacles, change at real time, while machine learning is used for obstacle detection. Several techniques can be applied to obtain routes in this context. We use a simulation technique which provides fast routes in real time. We apply this technique in the context of a school visit to the Alhambra Generalife Gardens in Granada (Spain) providing a model of the gardens’ map which evolves dynamically depending on the real time data offered by the machine learning module. This module is in charge of detecting overcrowded areas in order to avoid them. Routes are obtained by simulating several users traversing the map simultaneously and the fastest is returned. The system provided can be adapted to other user necessities and scenarios.
Key words :
Formal methods Intelligent routing system Machine learning Petri nets Simulation
Ref. laboratory citation :
misc-lab-413
Cross Ref. :
http://crossmark.crossref.org/dialog/?doi=10.1007/978-3-031-18516-8_16&domain=pdf
DOI :
10.1007/978-3-031-18516-8_16
Link :
Texte intégral
ACM :
B. Ahmed, B. M. Mounir, K. Oussama, M. Hermenegilda, D. Gregorio and C. Allaoua. 2022. An Example of a Dynamic CPN Model to Obtain Routes in the Presence of Obstacles Detected Using Machine Learning Techniques. In Proceedings of the Modelling and Implementation of Complex Systems, Mostaganem, Algeria. Lecture Notes in Networks and Systems, Chikhi Salim; Diaz-Descalzo Gregorio; Amine Abdelmalek; Chaoui Allaoua; Saidouni Djamel Eddine; Kholladi Mohamed Khireddine, Ed. (October 2022), Springer International Publishing, 220--233. DOI: https://doi.org/10.1007/978-3-031-18516-8_16.
APA :
Ahmed, B., Mounir, B. M., Oussama, K., Hermenegilda, M., Gregorio, D. & Allaoua, C. (2022, October). An Example of a Dynamic CPN Model to Obtain Routes in the Presence of Obstacles Detected Using Machine Learning Techniques. In Proceedings of the Modelling and Implementation of Complex Systems, Mostaganem, Algeria. In Chikhi Salim; Diaz-Descalzo Gregorio; Amine Abdelmalek; Chaoui Allaoua; Saidouni Djamel Eddine; Kholladi Mohamed Khireddine (Ed.). Lecture Notes in Networks and Systems, Springer International Publishing, 220--233. DOI: https://doi.org/10.1007/978-3-031-18516-8_16
IEEE :
B. Ahmed, B. M. Mounir, K. Oussama, M. Hermenegilda, D. Gregorio and C. Allaoua, "An Example of a Dynamic CPN Model to Obtain Routes in the Presence of Obstacles Detected Using Machine Learning Techniques". In Proceedings of the Modelling and Implementation of Complex Systems, Mostaganem, Algeria. Lecture Notes in Networks and Systems, Springer International Publishing, pp. 220--233, October, 2022. DOI: https://doi.org/10.1007/978-3-031-18516-8_16.
BibTeX :
@inproceedings{misc-lab-413,
author = {Ahmed, Bouzenada and Mounir, Bouhamed Mohammed and Oussama, Kamel and Hermenegilda, Maci\`{a} and Gregorio, Díaz and Allaoua, Chaoui},
title = {An Example of a Dynamic CPN Model to Obtain Routes in the Presence of Obstacles Detected Using Machine Learning Techniques},
booktitle = {Modelling and Implementation of Complex Systems},
series = {Lecture Notes in Networks and Systems},
location = {Mostaganem, Algeria},
isbn = {978-3-031-18516-8},
pages = {220----233},
editor = {Salim, Chikhi and Gregorio, Diaz-Descalzo and Abdelmalek, Amine and Allaoua, Chaoui and Eddine, Saidouni Djamel and Khireddine, Kholladi Mohamed},
publisher = {Springer International Publishing},
year = {2022},
month = {October},
doi = {10.1007/978-3-031-18516-8\_16},
url = {https://link.springer.com/chapter/10.1007/978-3-031-18516-8\_16},
crossref = {http://crossmark.crossref.org/dialog/?doi=10.1007/978-3-031-18516-8\_16&domain=pdf},
keywords = {Formal methods, Intelligent routing system, Machine learning, Petri nets, Simulation}
}
RIS :
TY  - CONF
TI - An Example of a Dynamic CPN Model to Obtain Routes in the Presence of Obstacles Detected Using Machine Learning Techniques
AU - B. Ahmed
AU - B. M. Mounir
AU - K. Oussama
AU - M. Hermenegilda
AU - D. Gregorio
AU - C. Allaoua
PY - 2022
BT - Modelling and Implementation of Complex Systems, Mostaganem, Algeria
SN - 978-3-031-18516-8
SP - 220
EP -
ED - C. Salim
ED - D.-D. Gregorio
ED - A. Abdelmalek
ED - C. Allaoua
ED - S. D. Eddine
ED - K. M. Khireddine
PB - Springer International Publishing
AB - Obtaining routes has been a challenge during the recent history of software systems. GPS has helped this technology to be widely used. Some challenges still remain like providing routes in the presence of obstacles. The term obstacle differs depending on the scenario. It is not the same for wheelchair users than for a group of school children. In this paper, we study how to enable a set of technologies to bridge this gap. We propose to combine a graphical formalism with machine learning. Color Petri Nets are used to model a dynamic scenario where conditions, i.e. obstacles, change at real time, while machine learning is used for obstacle detection. Several techniques can be applied to obtain routes in this context. We use a simulation technique which provides fast routes in real time. We apply this technique in the context of a school visit to the Alhambra Generalife Gardens in Granada (Spain) providing a model of the gardens’ map which evolves dynamically depending on the real time data offered by the machine learning module. This module is in charge of detecting overcrowded areas in order to avoid them. Routes are obtained by simulating several users traversing the map simultaneously and the fastest is returned. The system provided can be adapted to other user necessities and scenarios.
KW - Formal methods
KW - Intelligent routing system
KW - Machine learning
KW - Petri nets
KW - Simulation
DO - 10.1007/978-3-031-18516-8_16
UR - https://link.springer.com/chapter/10.1007/978-3-031-18516-8_16
ID - misc-lab-413
ER -