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A Multi-context BDI Recommender System: from Theory to Simulation

Amel Ben Othmane 1 Andrea G. B. Tettamanzi 2, 1 Serena Villata 1 Nhan Le Thanh 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In this paper, a simulation of a multi-agent recommender system is presented and developed in the NetLogo platform. The specification of this recommender system is based on the well known Belief-Desire-Intention agent architecture applied to multi-context systems, extended with contexts for additional reasoning abilities, especially social ones. The main goal of this simulation study is, besides illustrating the usefulness and feasibility of our agent-based recommender system in a realistic scenario, to understand how groups of agents behave in a social network compared to individual agents. Results show that agents within a social network have better collective performance than individual ones. The utility and the satisfaction of agents is increased by the exchange of messages when executing intentions
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Amel Ben Othmane, Andrea G. B. Tettamanzi, Serena Villata, Nhan Le Thanh. A Multi-context BDI Recommender System: from Theory to Simulation. Web Intelligence, Oct 2016, Omaha, United States. ⟨10.1109/WI.2016.0104⟩. ⟨hal-01400997⟩

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