CARS – A Spatio-Temporal BDI Recommender System: Time, Space and Uncertainty

Amel Othmane 1 Andrea G. B. Tettamanzi 2 Serena Villata 2 Nhan Le Thanh 2
2 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 : Agent-based recommender systems have been exploited in the last years to provide informative suggestions to users, showing the advantage of exploiting components like beliefs, goals and trust in the recommenda-tions' computation. However, many real-world scenarios, like the traffic one, require the additional feature of representing and reasoning about spatial and temporal knowledge, considering also their vague connotation. This paper tackles this challenge and introduces CARS, a spatio-temporal agent-based recommender system based on the Belief-Desire-Intention (BDI) architecture. Our approach extends the BDI model with spatial and temporal information to represent and reason about fuzzy beliefs and desires dynamics. An experimental evaluation about spatio-temporal reasoning in the traffic domain is carried out using the NetLogo platform, showing the improvements our recommender system introduces to support agents in achieving their goals.
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Amel Othmane, Andrea G. B. Tettamanzi, Serena Villata, Nhan Le Thanh. CARS – A Spatio-Temporal BDI Recommender System: Time, Space and Uncertainty. ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence, Jan 2018, Funchal, Madeira, Portugal. pp.1-10. ⟨hal-01721520⟩

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