Avoidance of Norm Violation in Multi-Agent Organizations

In contemporary society not adhering to norms is something which is unwanted. Currently approaches to prevent this from happening are often taken whereby the deviation of a norm is punished and possible repair actions are performed. However, this is a reactive approach and can only happen once the norm has already been violated. This paper presents a proactive approach which allows intervention before the deviation actually occurs. In order to do this, an approach is specified for agents that enforce norms and can influence the input states of agents. This approach includes learning of input/output correlations of these agents by constructing decision trees, and utilizing this decision tree to intervene such that norm violation can be avoided. The approach is evaluated in the domain of Criminology.