2023
Coelho, E.; Pimenta, N.; Peixoto, H.; Durães, D.; Melo-Pinto, P.; Alves, V.; Bandeira, L.; Machado, J.; Novais, P.
Multi-agent System for Multimodal Machine Learning Object Detection Proceedings Article
Em: de Pison F. J. Perez Garcia H. Garcia Bringas P., Martinez (Ed.): pp. 673-681, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 03029743, (cited By 0; Conference of Proceedings of the 18th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2023 ; Conference Date: 5 September 2023 Through 7 September 2023; Conference Code:299919).
Resumo | Links | BibTeX | Etiquetas: Agent approach; Complex problems; Learning objects; Machine learning problem; Machine-learning; Multi-modal; Multi-modality; Multimodal machine learning; Objects detection; Single-agent, Internet protocols; Machine learning; Network architecture; Object detection; Object recognition, Multi agent systems
@inproceedings{Coelho2023673,
title = {Multi-agent System for Multimodal Machine Learning Object Detection},
author = {E. Coelho and N. Pimenta and H. Peixoto and D. Durães and P. Melo-Pinto and V. Alves and L. Bandeira and J. Machado and P. Novais},
editor = {Martinez de Pison F. J. Perez Garcia H. Garcia Bringas P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172230717&doi=10.1007%2f978-3-031-40725-3_57&partnerID=40&md5=79d45a455c79da96394a5b0e05a56908},
doi = {10.1007/978-3-031-40725-3_57},
issn = {03029743},
year = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {14001 LNAI},
pages = {673-681},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Multi-agent systems have shown great promise in addressing complex problems that traditional single-agent approaches are not be able to handle. In this article, we propose a multi-agent system for the conception of a multimodal machine learning problem on edge devices. Our architecture leverages docker containers to encapsulate knowledge in the form of models and processes, enabling easy management of the system. Communication between agents is facilitated by Message Queuing Telemetry Transport, a lightweight messaging protocol ideal for Internet of Things and edge computing environments. Additionally, we highlight the significance of object detection in our proposed system, which is a crucial component of many multimodal machine learning tasks, by enabling the identification and localization of objects within diverse data modalities. In this manuscript an overall architecture description is performed, discussing the role of each agent and the communication protocol between them. The proposed system offers a general approach to multimodal machine learning problems on edge devices, demonstrating the advantages of multi-agent systems in handling complex and dynamic environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of Proceedings of the 18th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2023 ; Conference Date: 5 September 2023 Through 7 September 2023; Conference Code:299919},
keywords = {Agent approach; Complex problems; Learning objects; Machine learning problem; Machine-learning; Multi-modal; Multi-modality; Multimodal machine learning; Objects detection; Single-agent, Internet protocols; Machine learning; Network architecture; Object detection; Object recognition, Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Multi-agent systems have shown great promise in addressing complex problems that traditional single-agent approaches are not be able to handle. In this article, we propose a multi-agent system for the conception of a multimodal machine learning problem on edge devices. Our architecture leverages docker containers to encapsulate knowledge in the form of models and processes, enabling easy management of the system. Communication between agents is facilitated by Message Queuing Telemetry Transport, a lightweight messaging protocol ideal for Internet of Things and edge computing environments. Additionally, we highlight the significance of object detection in our proposed system, which is a crucial component of many multimodal machine learning tasks, by enabling the identification and localization of objects within diverse data modalities. In this manuscript an overall architecture description is performed, discussing the role of each agent and the communication protocol between them. The proposed system offers a general approach to multimodal machine learning problems on edge devices, demonstrating the advantages of multi-agent systems in handling complex and dynamic environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.