Social norms are powerful formalism in coordinating autonomous agents' behaviour to achieve certain objectives. In this paper, we propose a dynamic normative system to enable the reasoning of the changes of norms under different circumstances, which cannot be done in the existing static normative systems. We study two important problems (norm synthesis and norm recognition) related to the autonomy of the entire system and the agents, and characterise the computational complexities of solving these problems.
 In normative multi-agent systems, the question of "how an agent identifies norms in an open agent society" has not received much attention. This paper aims at addressing this question. To this end, this paper proposes an architecture for norm identification for an agent. The architecture is based on observation of interactions between agents. This architecture enables an autonomous agent to identify prohibition norms in a society using the prohibition norm identification (PNI) algorithm. The PNI algorithm uses association rule mining, a data mining approach to identify sequences of events as candidate norms. When a norm changes, an agent using our architecture will be able to modify the norm and also remove a norm if it does not hold in the society. Using simulations of a park scenario we demonstrate how an agent makes use of the norm identification framework to identify prohibition norms. When entering a system, an agent should be aware of the obligations and prohibitions (collectively norms) that will aect it. Several solutions to this norm identication problem have been proposed, which make use of observations of either other’s norm compliant, or norm violating, behaviour. These solutions fail in situations where norms are typically violated, or complied with, respectively. In this paper we propose a Bayesian approach to norm identication which operates by learning from both norm compliant and norm violating behaviour. By utilising both types of behaviour, our work not only overcomes a major limitation of existing approaches, but also yields improved performance over the state-of-the-art. We evaluate its eectiveness empirically, showing, under certain conditions, high accuracy scores.