an organizational multi-agent approach for designing ambient intelligence systems
| dc.contributor.author | abderrahim, sami | |
| dc.date.accessioned | 2026-09-29T08:48:35Z | |
| dc.date.available | 2026-09-29T08:48:35Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | This thesis explores the design and development of adaptive, intelligent environments using Multi-Agent Systems (MAS) within the paradigm of Ambient Intelligence (AmI). Focusing on organization-centered MAS development, we investigate how structured agent interactions, combined with artificial learning techniques, can enhance system robustness, scalability, and responsiveness to dynamic contexts. We leverage the Moise organizational modeling language and the JaCaMo framework to create a unified platform for developing and managing MAS, integrating agents, their environment, and organizational governance. This approach allows us to model complex social structures, define roles and norms, and specify collective goals, thereby facilitating coordinated agent behavior and adaptation to changing circumstances. The efficacy of our approach is demonstrated through two distinct case studies. The first addresses the timely prediction of COVID-19 infections by leveraging Internet of Medical Things (IoMT) data and a distributed multi-agent architecture incorporating machine learning algorithms. The second applies our framework to the challenging problem of real-time course timetable scheduling, demonstrating its adaptability to dynamic constraints and resource allocation. Our findings highlight the transformative potential of combining organizational paradigms with artificial learning in MAS, laying the foundation for more intelligent and adaptive AmI systems across diverse domains | |
| dc.identifier.uri | http://dspace.univ-khenchela.dz:4000/handle/123456789/11522 | |
| dc.language.iso | en | |
| dc.title | an organizational multi-agent approach for designing ambient intelligence systems | |
| dc.type | Thesis |