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.
Application of social norms Social norms are to regulate the behaviour of the stakeholders in a system, including sociotechnical system @cite_17 which has both humans and computers. They are used to represent the commitments (by e.g., business contracts, etc) between humans and organisations. The dynamic norms of this paper can be useful to model more realistic scenarios in which commitments may be changed with the environmental changes.
@cite_17
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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. Norms and social laws are one of the key mechanisms used to facilitate coordination in multiagent systems. In existing approaches the process of designing useful norms has to either be performed by a human expert, or requires a full enumeration of the state space which is bound to cause tractability problems in non-trivial domains. In this paper we propose a novel automated synthesis procedure for prohibitive norms in planning-based domains that disallow access to a set of predefined undesirable states. Our method performs local search around declarative specifications of states using AI planning methods. Using this approach, norms can be synthesised in a generalised way over incomplete state specifications to improve the efficiency of the process in many practical cases, while producing concise, generalised, social norms that are applicable to entire sets of system states. We present an algorithm that utilises traditional planning techniques to ensure continued accessibility under the prohibitions introduced by norms. An analysis of the computational properties of our algorithm is presented together with a discussion of possible heuristic improvements. In multi-agent systems, software agents are modelled to possess characteristics and behaviour borrowed from human societies. Norms are expectations of behaviours of the agents in a society. Norms can be established in a society in different ways. In human societies, there are several types of norms such as moral norms, social norms and legal norms (laws). In artificial agent societies, the designers can impose these norms on the agents. Being autonomous, agents might not always follow the norms. Monitoring and controlling mechanisms should be in place to enforce norms. As the agents are autonomous, they themselves can evolve new norms while adapting to changing needs. In order to design and develop robust artificial agent societies, it is important to understand different approaches proposed by researchers by which norms can spread and emerge within agent societies. This paper makes two contributions to the study of norms. Firstly, based on the simulation works on norms, we propose a life-cycle model for norms. Secondly, we discuss different mechanisms used by researchers to study norm creation, spreading, enforcement and emergence. 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.