Nowadays mobile devices, such as smartphones, are becoming increasingly popular and they offer a wide range of mobile applications, also called mobile apps, suitable for different situations. This abundance of applications makes the research over them difficult and time consuming.
Context-aware resource recommendation for ubiquitous devices is aimed at proactively pushing personalized suggestions to mobile users, presenting them unseen mobile apps. Typically, the recommendation is based on recognizing the current situation of the user and suggesting them the appropriate resources for those situations. We believe that recommendation schemes can emerge from users’ collective behavior.
An emergent behavior or emergent property can appear when a number of simple entities (agents) operate in an environment, forming more complex behaviors as a collective. In our case the entities are represented by mobile users who provide positional data through Global Positioning System (GPS) provided by almost all modern smartphones.
The recognition task is performed by exploiting contextual information and preferably without using any explicit input from the user. Thus, in this thesis we present a collaborative multi-agent scheme for social events detection in which a stigmergic paradigm and fuzzy representations are employed to cope with the approximation typical of implicit and aggregated information.
The multi-agent scheme is structured into three levels of information processing. The first level is based on a stigmergic paradigm, in which marking agents, following the mobile user, leave marks in the environment. The accumulation of such marks enables the second level, a fuzzy information granulation process, managed by event agents, in which relevant events can emerge. Finally, the third level, a fuzzy inference process, managed by situation agents deduces the user situation from the underlying events.