Semantic-based Social Intelligence through Multi-Agent Systems

Current technologies and market solutions are far from fulfilling the Ambient Intelligence (AmI) vision of simplified people-environment interactions. Even though Despite recent solutions based on Internet of Things (IoT) technologies provide the needed infrastructure, most approaches suffer from inadequate levels of intelligence and autonomy. This paper proposes a novel semantic-based Multi-Agent System (MAS) framework complying with the emerging Social Internet of Things paradigm devoted to improve both automation and adaptivity: device agents self-organize in social relationships, interacting autonomously and sharing information, cooperating and orchestrating ambient resources. A service-oriented architecture allows collaborative dissemination, discovery and composition of service/resource descriptions. Decision and choreography capabilities of software agents leverage Semantic Web languages at the knowledge representation layer and a mobile-oriented implementation of non-standard inferences for semantic matchmaking. Benefits of the proposal are highlighted through an AmI case study in the field of Home and Building Automation (HBA). A comparison with the state of the art is also provided.

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