Cognitive Social Learners: An Architecture for Modeling Normative Behavior


  • Rahmatollah Beheshti University of Central Florida
  • Awrad Mohammed Ali University of Central Florida
  • Gita Sukthankar University of Central Florida



normative multiagent systems, social simulation, BDI architectures, social learning


In many cases, creating long-term solutions to sustainability issues requires not only innovative technology, but also large-scale public adoption of the proposed solutions. Social simulations are a valuable but underutilized tool that can help public policy researchers understand when sustainable practices are likely to make the delicate transition from being an individual choice to becoming a social norm. In this paper, we introduce a new normative multi-agent architecture, Cognitive Social Learners (CSL), that models bottom-up norm emergence through a social learning mechanism, while using BDI (Belief/Desire/Intention) reasoning to handle adoption and compliance. CSL preserves a greater sense of cognitive realism than influence propagation or infectious transmission approaches, enabling the modeling of complex beliefs and contradictory objectives within an agent-based simulation. In this paper, we demonstrate the use of CSL for modeling norm emergence of recycling practices and public participation in a smoke-free campus initiative.




How to Cite

Beheshti, R., Mohammed Ali, A., & Sukthankar, G. (2015). Cognitive Social Learners: An Architecture for Modeling Normative Behavior. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



AAAI Technical Track: Multiagent Systems