Cognitive Social Learners: An Architecture for Modeling Normative Behavior

被引:0
|
作者
Beheshti, Rahmatollah [1 ]
Ali, Awrad Mohammed [1 ]
Sukthankar, Gita [1 ]
机构
[1] Univ Cent Florida, Dept EECS, Orlando, FL 32816 USA
关键词
EMERGENCE; NORMS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:2017 / 2023
页数:7
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