Skip to Main content Skip to Navigation
Conference papers

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
Complete list of metadatas

Cited literature [12 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01400997
Contributor : Amel Ben Othmane <>
Submitted on : Thursday, June 1, 2017 - 10:44:37 AM
Last modification on : Tuesday, May 26, 2020 - 6:50:41 PM
Document(s) archivé(s) le : Wednesday, September 6, 2017 - 6:32:35 PM

File

Web_Intelligence_2016.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

933

Files downloads

289