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Future robots should follow human social norms in order to be useful andaccepted in human society. In this paper, we leverage already existing socialknowledge in human societies by capturing it in our framework through thenotion of social norms. We show how norms can be used to guide a reinforcementlearning agent towards achieving normative behavior and apply the same set ofnorms over different domains. Thus, we are able to: (1) provide a way tointuitively encode social knowledge (through norms); (2) guide learning towardsnormative behaviors (through an automatic norm reward system); and (3) achievea transfer of learning by abstracting policies; Finally, (4) the method is notdependent on a particular RL algorithm. We show how our approach can be seen asa means to achieve abstract representation and learn procedural knowledge basedon the declarative semantics of norms and discuss possible implications of thisin some areas of cognitive science.

Robby is Not a Robber (anymore): On the Use of Institutions for Learning Normative Behavior