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.
Mots clés :
Formal methods
Intelligent routing system
Machine learning
Petri nets
Simulation
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 -