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.
Norm recognition. Norm recognition can be related to the norm learning problem, which employs various approaches, such as data mining @cite_18 and sampling and parsing @cite_5 @cite_15 , for the agent to learn social norms by observing other agents' behaviour. On the other hand, our norm recognition problems are based on formal verification, aiming to decide whether the agents are designed well so that they can recognise the current normative system from a set of possible ones. We also study the complexity of them.
@cite_5 @cite_18 @cite_15
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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. Social norms play a pivotal role in sustaining social order by regulating individual behaviors in a society. In normative multiagent systems, social norms have been used as an efficient mechanism to govern virtual agent societies towards cooperation and coordination. In this paper, we study the emergence of social norms via learning from repeated local interactions in networked agent societies. We propose a collective learning framework, which imitates the opinion aggregation process in human decision making, to study the impact of agent local collective behaviors on norm emergence in different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action towards each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. We conduct extensive experiments to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, and so on. Experimental results reveal some significant insights into norm emergence in networked agent societies achieved through local collective behaviors. Norms are an important part of human social systems, governing many aspects of group decision-making. Yet many popularly used social models neglect to model normative effects on human behavior, relying on simple probabilistic and majority voting models of influence diffusion. Within the multi-agent research community, the study of norm emergence, compliance, and adoption has resulted in new architectures and standards for normative agents; however few of these models have been successfully applied to real-world public policy problems. In this paper, we propose a new lightweight architecture for constructing normative agents to model human social systems; the aim of our research is to be able to study the effects of different public policy decisions on a community. Here we present a case study showing the usage of our architecture for predicting trends in smoking cessation resulting from a smoke-free campus initiative. Our agent-based model combines social, environmental, and personal factors to accurately predict smoking trends and attitudes. The performance of both the whole and ablated model is evaluated against statistics from an independent source.