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Learning Normative Behaviors Through Abstraction

. Future robots should follow human social norms to be useful and accepted in human society. In this paper, we show how prior knowledge about social norms, represented using an exist-ing normative framework, can be used to (1) guide reinforcement learning agents towards normative policies, and (2) re-use (transfer) learned policies in novel domains. The proposed method is not de-pendent on a particular reinforcement learning algorithm and can be seen as a means to learn abstract procedural knowledge based on declarative domain-independent semantic speciﬁcations.