Proceedings of the Annual Meeting of the Cognitive Science Society

Human behavior is frequently guided by social and moralnorms; in fact, no societies, no social groups could exist with-out norms. However, there are few cognitive science ap-proaches to this central phenomenon of norms. While therehas been some progress in developing formal representationsof norm systems (e.g., deontological approaches), we do notyet know basic properties of human norms: how they arerepresented, activated, and learned. Further, what computa-tional models can capture these properties, and what algo-rithms could learn them? In this paper we describe initial ex-periments on human norm representations in which the contextspecificity of norms features prominently. We then provide aformal representation of norms using Dempster-Shafer Theorythat allows a machine learning algorithm to learn norms un-der uncertainty from these human data, while preserving theircontext specificity.