Learning Context-Sensitive Norms under Uncertainty

Abstract

Norms and conventions play a central role in maintaining social order in multi-agent societies [2, 5]. I study the problem of how these norms and conventions can be learned from observation of heterogeneous sources, under conditions of uncertainty. This is necessary as it is not enough to simply hard code a set of norms into a new agent prior to entering society because norms can evolve over time as agents enter and leave the society [9].